From 6167331bac707087a3aad8aba1993a91a2c42080 Mon Sep 17 00:00:00 2001
From: jacopo-ronchi <jacopo-ronchi@users.noreply.github.com>
Date: Thu, 30 Nov 2023 16:38:16 +0000
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diff --git a/404.html b/404.html
index 0f5a872..b89ce70 100644
--- a/404.html
+++ b/404.html
@@ -86,7 +86,7 @@
 
 <div class="pkgdown-footer-right">
   <p></p>
-<p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer>
diff --git a/CODE_OF_CONDUCT.html b/CODE_OF_CONDUCT.html
index 2be3bd0..7fc62de 100644
--- a/CODE_OF_CONDUCT.html
+++ b/CODE_OF_CONDUCT.html
@@ -70,7 +70,7 @@
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/CONTRIBUTING.html b/CONTRIBUTING.html
index 57610f8..29c1620 100644
--- a/CONTRIBUTING.html
+++ b/CONTRIBUTING.html
@@ -87,7 +87,7 @@ <h2 id="code-of-conduct">Code of Conduct<a class="anchor" aria-label="anchor" hr
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/LICENSE.html b/LICENSE.html
index ce1be72..76fbcfe 100644
--- a/LICENSE.html
+++ b/LICENSE.html
@@ -253,7 +253,7 @@ <h2 id="how-to-apply-these-terms-to-your-new-programs">How to Apply These Terms
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/SUPPORT.html b/SUPPORT.html
index c387ae0..fc3bc89 100644
--- a/SUPPORT.html
+++ b/SUPPORT.html
@@ -85,7 +85,7 @@ <h2 id="what-happens-next">What happens next?<a class="anchor" aria-label="ancho
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/articles/MIRit.html b/articles/MIRit.html
index ef6544e..5d19d20 100644
--- a/articles/MIRit.html
+++ b/articles/MIRit.html
@@ -5,6 +5,7 @@
 <meta charset="utf-8">
 <meta name="generator" content="pandoc">
 <meta http-equiv="X-UA-Compatible" content="IE=EDGE">
+<meta name="date" content="2023-11-30">
 <title>Integrate miRNA and gene expression data with MIRit</title>
 <script src="MIRit_files/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1">
 <link href="MIRit_files/bootstrap-3.3.5/css/bootstrap.min.css" rel="stylesheet">
@@ -168,7 +169,7 @@
   margin-left: 2em;
 }
 </style>
-<link rel="stylesheet" href="/home/jacopo/R/x86_64-pc-linux-gnu-library/4.3/BiocStyle/resources/html/bioconductor.css" type="text/css">
+<link rel="stylesheet" href="/__w/_temp/Library/BiocStyle/resources/html/bioconductor.css" type="text/css">
 <style type="text/css">
 .main-container {
   max-width: 828px;
@@ -342,7 +343,7 @@ <h1 class="title toc-ignore">Integrate miRNA and gene expression data with MIRit
 <p class="author-name">Jacopo Ronchi<span class="affil-mark">1*</span></p>
 <p class="author-affiliation"><span class="affil-mark">1</span>University of Milano-Bicocca, Italy</p>
 <p class="author-email"><span class="affil-mark">*</span><a href="mailto:jacopo.ronchi@unimib.it">jacopo.ronchi@unimib.it</a></p>
-<h4 class="date">30 novembre 2023</h4>
+<h4 class="date">30 November 2023</h4>
 <h4 class="abstract">Abstract</h4>
 <p>In this vignette, we are going to see how to use MIRit for investigating the compromised miRNA-gene regulatory networks in thyroid cancer. In particular, an RNA-Seq experiment will be used as an example to demonstrate how to perform an integrative analysis with MIRit, including differential expression analysis, functional enrichment and characterization, correlation analysis and, lastly, the construction and visualization of the impaired miRNAs regulatory axes within biological pathways.</p>
 <h4 class="package">Package</h4>
@@ -439,7 +440,7 @@ <h3 id="set-up-expression-matrices">
 <span class="header-section-number">2.3</span> Set up expression matrices<a class="anchor" aria-label="anchor" href="#set-up-expression-matrices"></a>
 </h3>
 <p>As input data, MIRit requires miRNA and gene expression measurements as matrices with samples as columns, and genes/miRNAs as rows. Further, the row names of miRNA expression matrix should contain miRNA names according to miRBase nomenclature (e.g. hsa-miR-151a-5p, hsa-miR-21-5p), whereas for gene expression matrix, row names must contain gene symbols according to HGNC (e.g. TYK2, BDNF, NTRK2).</p>
-<p>These matrices may handle different types of values deriving from multiple technologies, including microarrays, RNA-Seq and proteomics. The only requirement is that, for microarray studies, expression matrices must be normalized and log2 transformed. This can be achieved by applying the RMA algorithm implemented in the <em><a href="https://bioconductor.org/packages/3.17/oligo" class="external-link">oligo</a></em> <span class="citation">(<a href="#ref-carvalho_framework_2010">Carvalho and Irizarry 2010</a>)</span> package, or by applying other quantile normalization strategies. On the contrary, for RNA-Seq and miRNA-Seq experiments, the simple count matrix must be supplied.</p>
+<p>These matrices may handle different types of values deriving from multiple technologies, including microarrays, RNA-Seq and proteomics. The only requirement is that, for microarray studies, expression matrices must be normalized and log2 transformed. This can be achieved by applying the RMA algorithm implemented in the <em><a href="https://bioconductor.org/packages/3.19/oligo" class="external-link">oligo</a></em> <span class="citation">(<a href="#ref-carvalho_framework_2010">Carvalho and Irizarry 2010</a>)</span> package, or by applying other quantile normalization strategies. On the contrary, for RNA-Seq and miRNA-Seq experiments, the simple count matrix must be supplied.</p>
 <p>Eventually, expression matrices required by MIRit should appear as those in <code>mirnaCounts</code> and <code>geneCounts</code>, which are displayed in Tables <a href="#tab:geneExpr">1</a> and <a href="#tab:mirnaExpr">2</a>.</p>
 <div class="horizontal-scroll"><table class="table">
 <caption>
@@ -648,11 +649,11 @@ <h3 id="perform-mirna-and-gene-differential-expression">
 <h4 id="deMethods">
 <span class="header-section-number">3.2.1</span> Available methods for RNA-Seq and microarrays<a class="anchor" aria-label="anchor" href="#deMethods"></a>
 </h4>
-<p>Now, we are ready to perform differential expression analysis. In this concern, MIRit provides different options based on the technology used for generating expression data. Indeed, when expression measurements derive from microarrays, MIRit calculates differentially expressed features through the pipeline implemented in the <em><a href="https://bioconductor.org/packages/3.17/limma" class="external-link">limma</a></em> package. On the other hand, when expression values derive from RNA-Seq experiments, MIRit allows to choose between different approaches, including:</p>
+<p>Now, we are ready to perform differential expression analysis. In this concern, MIRit provides different options based on the technology used for generating expression data. Indeed, when expression measurements derive from microarrays, MIRit calculates differentially expressed features through the pipeline implemented in the <em><a href="https://bioconductor.org/packages/3.19/limma" class="external-link">limma</a></em> package. On the other hand, when expression values derive from RNA-Seq experiments, MIRit allows to choose between different approaches, including:</p>
 <ul>
-<li>the approach defined in the <em><a href="https://bioconductor.org/packages/3.17/edgeR" class="external-link">edgeR</a></em> package;</li>
-<li>the approach defined in the <em><a href="https://bioconductor.org/packages/3.17/DESeq2" class="external-link">DESeq2</a></em> package;</li>
-<li>the <code>limma-voom</code> approach defined in the <em><a href="https://bioconductor.org/packages/3.17/limma" class="external-link">limma</a></em> package.</li>
+<li>the approach defined in the <em><a href="https://bioconductor.org/packages/3.19/edgeR" class="external-link">edgeR</a></em> package;</li>
+<li>the approach defined in the <em><a href="https://bioconductor.org/packages/3.19/DESeq2" class="external-link">DESeq2</a></em> package;</li>
+<li>the <code>limma-voom</code> approach defined in the <em><a href="https://bioconductor.org/packages/3.19/limma" class="external-link">limma</a></em> package.</li>
 </ul>
 <p>Moreover, MIRit gives the possibility of fully customizing the parameters used for differential expression analysis, thus allowing a finer control that makes it easy to adopt strategies that differ from the standard pipelines proposed in these packages. For additional information, see Section <a href="#param">3.2.4</a>.</p>
 </div>
@@ -681,7 +682,7 @@ <h4 id="the-performmirnade-and-performgenede-functions">
 <li>
 <code>design</code>, which specifies the linear model with the variable of interest and eventual covariates. To do so, we pass to this parameter the R formula that we defined in Section <a href="#model">3.2.2</a>.</li>
 <li>
-<code>method</code>, which tells MIRit which pipeline we want to use for computing differentially expressed features. As stated in Section <a href="#deMethods">3.2.1</a>, for microarray studies the only option available is <code>limma</code>, while for RNA-Seq experiments, the user can choose between <code>edgeR</code>, <code>DESeq2</code>, and <code>voom</code> (for limma-voom). In our case we are going to perform differential expression analysis through the pipeline implemented in the <em><a href="https://bioconductor.org/packages/3.17/edgeR" class="external-link">edgeR</a></em> package.</li>
+<code>method</code>, which tells MIRit which pipeline we want to use for computing differentially expressed features. As stated in Section <a href="#deMethods">3.2.1</a>, for microarray studies the only option available is <code>limma</code>, while for RNA-Seq experiments, the user can choose between <code>edgeR</code>, <code>DESeq2</code>, and <code>voom</code> (for limma-voom). In our case we are going to perform differential expression analysis through the pipeline implemented in the <em><a href="https://bioconductor.org/packages/3.19/edgeR" class="external-link">edgeR</a></em> package.</li>
 </ul>
 <p>Following our example, let’s calculate differentially expressed genes and differentially expressed miRNAs in thyroid cancer.</p>
 <div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
@@ -854,7 +855,7 @@ <h3 id="available-approaches-ora-gsea-and-camera">
 <h3 id="categories">
 <span class="header-section-number">4.2</span> Available databases and categories<a class="anchor" aria-label="anchor" href="#categories"></a>
 </h3>
-<p>As described above, functional enrichment analysis relies on gene sets, which consist in collections of genes that are annotated to specific functions or terms. Independently from the strategy used for the analysis, functional enrichment methods need access to these properly curated collections of genes. In the effort of providing access to a vast number of these resources, MIRit uses the <em><a href="https://bioconductor.org/packages/3.17/geneset" class="external-link">geneset</a></em> package to support multiple databases, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), MsigDB, WikiPathways, Reactome, Enrichr, Disease Ontology (DO), Network of Cancer Genes (NCG), DisGeNET, and COVID-19. However, the majority of these databases contain multiple categories. To see the complete list of available gene sets for each database refer to the documentation of the <code><a href="../reference/enrichGenes.html">enrichGenes()</a></code> function.</p>
+<p>As described above, functional enrichment analysis relies on gene sets, which consist in collections of genes that are annotated to specific functions or terms. Independently from the strategy used for the analysis, functional enrichment methods need access to these properly curated collections of genes. In the effort of providing access to a vast number of these resources, MIRit uses the <em><a href="https://bioconductor.org/packages/3.19/geneset" class="external-link">geneset</a></em> package to support multiple databases, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), MsigDB, WikiPathways, Reactome, Enrichr, Disease Ontology (DO), Network of Cancer Genes (NCG), DisGeNET, and COVID-19. However, the majority of these databases contain multiple categories. To see the complete list of available gene sets for each database refer to the documentation of the <code><a href="../reference/enrichGenes.html">enrichGenes()</a></code> function.</p>
 </div>
 <div class="section level3" number="4.3">
 <h3 id="supported-species">
@@ -904,36 +905,6 @@ <h3 id="enrichment">
 <span><span class="co">#&gt; Since not specified, 'category' for KEGG database is set to pathway (default).</span></span>
 <span><span class="co">#&gt; Preparing the appropriate gene set...</span></span>
 <span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt; Registered S3 methods overwritten by 'treeio':</span></span>
-<span><span class="co">#&gt;   method              from    </span></span>
-<span><span class="co">#&gt;   MRCA.phylo          tidytree</span></span>
-<span><span class="co">#&gt;   MRCA.treedata       tidytree</span></span>
-<span><span class="co">#&gt;   Nnode.treedata      tidytree</span></span>
-<span><span class="co">#&gt;   Ntip.treedata       tidytree</span></span>
-<span><span class="co">#&gt;   ancestor.phylo      tidytree</span></span>
-<span><span class="co">#&gt;   ancestor.treedata   tidytree</span></span>
-<span><span class="co">#&gt;   child.phylo         tidytree</span></span>
-<span><span class="co">#&gt;   child.treedata      tidytree</span></span>
-<span><span class="co">#&gt;   full_join.phylo     tidytree</span></span>
-<span><span class="co">#&gt;   full_join.treedata  tidytree</span></span>
-<span><span class="co">#&gt;   groupClade.phylo    tidytree</span></span>
-<span><span class="co">#&gt;   groupClade.treedata tidytree</span></span>
-<span><span class="co">#&gt;   groupOTU.phylo      tidytree</span></span>
-<span><span class="co">#&gt;   groupOTU.treedata   tidytree</span></span>
-<span><span class="co">#&gt;   inner_join.phylo    tidytree</span></span>
-<span><span class="co">#&gt;   inner_join.treedata tidytree</span></span>
-<span><span class="co">#&gt;   is.rooted.treedata  tidytree</span></span>
-<span><span class="co">#&gt;   nodeid.phylo        tidytree</span></span>
-<span><span class="co">#&gt;   nodeid.treedata     tidytree</span></span>
-<span><span class="co">#&gt;   nodelab.phylo       tidytree</span></span>
-<span><span class="co">#&gt;   nodelab.treedata    tidytree</span></span>
-<span><span class="co">#&gt;   offspring.phylo     tidytree</span></span>
-<span><span class="co">#&gt;   offspring.treedata  tidytree</span></span>
-<span><span class="co">#&gt;   parent.phylo        tidytree</span></span>
-<span><span class="co">#&gt;   parent.treedata     tidytree</span></span>
-<span><span class="co">#&gt;   root.treedata       tidytree</span></span>
-<span><span class="co">#&gt;   rootnode.phylo      tidytree</span></span>
-<span><span class="co">#&gt;   sibling.phylo       tidytree</span></span>
 <span><span class="co">#&gt; Some ID occurs one-to-many match, like "79154, 7920, 79143"...</span></span>
 <span><span class="co">#&gt; 99.96% genes are mapped to symbol</span></span>
 <span><span class="co">#&gt; Ranking genes based on signed.pval...</span></span>
@@ -978,9 +949,9 @@ <h4 id="access-results-table">
 <td align="left">Protein processing in endoplasmic reticulum</td>
 <td align="right">0</td>
 <td align="right">0.02</td>
-<td align="right">0.52</td>
+<td align="right">0.54</td>
 <td align="right">-0.54</td>
-<td align="right">-1.87</td>
+<td align="right">-1.85</td>
 <td align="right">55</td>
 <td align="left">BCL2, HY….</td>
 </tr>
@@ -988,9 +959,9 @@ <h4 id="access-results-table">
 <td align="left">Thyroid hormone synthesis</td>
 <td align="right">0</td>
 <td align="right">0.02</td>
-<td align="right">0.54</td>
+<td align="right">0.52</td>
 <td align="right">-0.67</td>
-<td align="right">-2.02</td>
+<td align="right">-2.01</td>
 <td align="right">27</td>
 <td align="left">SLC26A4,….</td>
 </tr>
@@ -1112,7 +1083,7 @@ <h2 id="investigate-the-effects-of-mirna-expression-changes-on-target-genes">
 <li>
 <strong>Association tests</strong>, like Fisher’s exact test and Boschloo’s exact test;</li>
 <li>
-<strong>Rotation gene-set tests</strong>, as implemented in the <code>fry</code> function from <em><a href="https://bioconductor.org/packages/3.17/limma" class="external-link">limma</a></em> package.</li>
+<strong>Rotation gene-set tests</strong>, as implemented in the <code>fry</code> function from <em><a href="https://bioconductor.org/packages/3.19/limma" class="external-link">limma</a></em> package.</li>
 </ul>
 <p>For unpaired data, only association tests and rotation gene-set tests are available, whereas correlation analysis is the best performing strategy for paired data. The integrative analysis, either performed through correlation, association tests, or rotation gene-set tests, is implemented in the <code><a href="../reference/mirnaIntegration.html">mirnaIntegration()</a></code> function. When using the default option <code>test = "auto"</code>, MIRit automatically performs the appropriate test for paired and unpaired samples. If only some samples of the data set have paired measurements, a correlation analysis will be carried out only for those subjects.</p>
 <div class="section level3" number="7.1">
@@ -1302,7 +1273,7 @@ <h4 id="perform-one-sideded-association-tests-in-mirit">
 <h3 id="rotation-gene-set-tests-for-unpaired-data">
 <span class="header-section-number">7.3</span> Rotation gene-set tests for unpaired data<a class="anchor" aria-label="anchor" href="#rotation-gene-set-tests-for-unpaired-data"></a>
 </h3>
-<p>Lastly, for unpaired data, the effect of DE-miRNAs on the expression of target genes can be estimated through rotation gene-set tests. In this approach, we want to evaluate for each miRNA whether its target genes tend to be differentially expressed in the opposite direction. In particular, a fast approximation to rotation gene-set testing called <code>fry</code>, implemented in the <em><a href="https://bioconductor.org/packages/3.17/limma" class="external-link">limma</a></em> package, can be used to statistically quantify the influence of miRNAs on expression changes of their target genes.</p>
+<p>Lastly, for unpaired data, the effect of DE-miRNAs on the expression of target genes can be estimated through rotation gene-set tests. In this approach, we want to evaluate for each miRNA whether its target genes tend to be differentially expressed in the opposite direction. In particular, a fast approximation to rotation gene-set testing called <code>fry</code>, implemented in the <em><a href="https://bioconductor.org/packages/3.19/limma" class="external-link">limma</a></em> package, can be used to statistically quantify the influence of miRNAs on expression changes of their target genes.</p>
 <p>To perform the integrative analysis through rotation gene-set tests, we must simply set <code>test = "fry"</code> when calling <code><a href="../reference/mirnaIntegration.html">mirnaIntegration()</a></code> function.</p>
 <div class="sourceCode" id="cb27"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">## perform the integrative analysis through 'fry' method</span></span>
@@ -1386,7 +1357,7 @@ <h3 id="topologically-aware-integrative-pathway-analysis-taipa">
 <h3 id="perform-taipa-in-mirit">
 <span class="header-section-number">8.2</span> Perform TAIPA in MIRit<a class="anchor" aria-label="anchor" href="#perform-taipa-in-mirit"></a>
 </h3>
-<p>Before performing TAIPA, we need to create miRNA-augmented networks. To do so, MIRit implements the <code><a href="../reference/preparePathways.html">preparePathways()</a></code> function, which automatically uses the <em><a href="https://bioconductor.org/packages/3.17/graphite" class="external-link">graphite</a></em> R package to download biological networks from multiple pathway databases, namely <code>KEGG</code>, <code>WikiPathways</code> and <code>Reactome</code>. Then, each pathway is converted to a <code>graph</code> object and significant miRNA-mRNA pairs are added to the network. Further, edge weights are included according to interaction type. After running this function, we obtain a <code>list</code> containing all the miRNA-augmented nwtworks as <code>graph</code> objects.</p>
+<p>Before performing TAIPA, we need to create miRNA-augmented networks. To do so, MIRit implements the <code><a href="../reference/preparePathways.html">preparePathways()</a></code> function, which automatically uses the <em><a href="https://bioconductor.org/packages/3.19/graphite" class="external-link">graphite</a></em> R package to download biological networks from multiple pathway databases, namely <code>KEGG</code>, <code>WikiPathways</code> and <code>Reactome</code>. Then, each pathway is converted to a <code>graph</code> object and significant miRNA-mRNA pairs are added to the network. Further, edge weights are included according to interaction type. After running this function, we obtain a <code>list</code> containing all the miRNA-augmented nwtworks as <code>graph</code> objects.</p>
 <p>In our example, we want to use the significant miRNA-target pairs that we identified in Section <a href="#correlation">7.1.2</a> to augment biological pathways retrieved from the KEGG database.</p>
 <div class="sourceCode" id="cb29"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">## create miRNA-augmented networks using KEGG pathways</span></span>
@@ -1411,7 +1382,7 @@ <h3 id="perform-taipa-in-mirit">
 <h3 id="c-code-and-parallel-computing-with-biocparallel">
 <span class="header-section-number">8.3</span> C++ code and parallel computing with <code>BiocParallel</code><a class="anchor" aria-label="anchor" href="#c-code-and-parallel-computing-with-biocparallel"></a>
 </h3>
-<p>For computational efficiency, pathway score computation has been implemented in C++ language. Furthermore, since computing pathway score for 10000 networks for each pathway is computationally intensive, parallel computing has been employed to reduce running time. The user can modify the parallel computing behavior by specifying the <code>BPPARAM</code> parameter. See the help page of the <em><a href="https://bioconductor.org/packages/3.17/BiocParallel" class="external-link">BiocParallel</a></em> package for further details. Both the <code><a href="../reference/preparePathways.html">preparePathways()</a></code> and the <code><a href="../reference/topologicalAnalysis.html">topologicalAnalysis()</a></code> functions accept the <code>BPPARAM</code> option.</p>
+<p>For computational efficiency, pathway score computation has been implemented in C++ language. Furthermore, since computing pathway score for 10000 networks for each pathway is computationally intensive, parallel computing has been employed to reduce running time. The user can modify the parallel computing behavior by specifying the <code>BPPARAM</code> parameter. See the help page of the <em><a href="https://bioconductor.org/packages/3.19/BiocParallel" class="external-link">BiocParallel</a></em> package for further details. Both the <code><a href="../reference/preparePathways.html">preparePathways()</a></code> and the <code><a href="../reference/topologicalAnalysis.html">topologicalAnalysis()</a></code> functions accept the <code>BPPARAM</code> option.</p>
 </div>
 <div class="section level3" number="8.4">
 <h3 id="visualize-the-significantly-affected-pathways">
@@ -1452,121 +1423,125 @@ <h3 id="visualize-the-impaired-networks-within-biological-pathways">
 <div class="section level2">
 <h2 id="session-info">Session info<a class="anchor" aria-label="anchor" href="#session-info"></a>
 </h2>
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-<span><span class="co">#&gt; Platform: x86_64-pc-linux-gnu (64-bit)</span></span>
-<span><span class="co">#&gt; Running under: Ubuntu 20.04.6 LTS</span></span>
+<pre><code><span><span class="co">#&gt; R Under development (unstable) (2023-11-22 r85609)</span></span>
+<span><span class="co">#&gt; Platform: x86_64-pc-linux-gnu</span></span>
+<span><span class="co">#&gt; Running under: Ubuntu 22.04.3 LTS</span></span>
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+<span><span class="co">#&gt; BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 </span></span>
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+<span><span class="co">#&gt;  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              </span></span>
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+<span><span class="co">#&gt;  [53] tidytree_0.4.5                codetools_0.2-19             </span></span>
+<span><span class="co">#&gt;  [55] DelayedArray_0.29.0           xml2_1.3.5                   </span></span>
+<span><span class="co">#&gt;  [57] DOSE_3.29.0                   ggforce_0.4.1                </span></span>
+<span><span class="co">#&gt;  [59] tidyselect_1.2.0              aplot_0.2.2                  </span></span>
+<span><span class="co">#&gt;  [61] farver_2.1.1                  viridis_0.6.4                </span></span>
+<span><span class="co">#&gt;  [63] matrixStats_1.1.0             stats4_4.4.0                 </span></span>
+<span><span class="co">#&gt;  [65] BiocFileCache_2.11.1          jsonlite_1.8.7               </span></span>
+<span><span class="co">#&gt;  [67] fst_0.9.8                     ellipsis_0.3.2               </span></span>
+<span><span class="co">#&gt;  [69] tidygraph_1.2.3               progress_1.2.2               </span></span>
+<span><span class="co">#&gt;  [71] tools_4.4.0                   treeio_1.27.0                </span></span>
+<span><span class="co">#&gt;  [73] HPO.db_0.99.2                 Rcpp_1.0.11                  </span></span>
+<span><span class="co">#&gt;  [75] glue_1.6.2                    gridExtra_2.3                </span></span>
+<span><span class="co">#&gt;  [77] SparseArray_1.3.1             BiocBaseUtils_1.5.0          </span></span>
+<span><span class="co">#&gt;  [79] xfun_0.41                     usethis_2.2.2                </span></span>
+<span><span class="co">#&gt;  [81] qvalue_2.35.0                 MatrixGenerics_1.15.0        </span></span>
+<span><span class="co">#&gt;  [83] GenomeInfoDb_1.39.1           dplyr_1.1.4                  </span></span>
+<span><span class="co">#&gt;  [85] withr_2.5.2                   BiocManager_1.30.22          </span></span>
+<span><span class="co">#&gt;  [87] fastmap_1.1.1                 fansi_1.0.5                  </span></span>
+<span><span class="co">#&gt;  [89] callr_3.7.3                   digest_0.6.33                </span></span>
+<span><span class="co">#&gt;  [91] R6_2.5.1                      mime_0.12                    </span></span>
+<span><span class="co">#&gt;  [93] gridGraphics_0.5-1            colorspace_2.1-0             </span></span>
+<span><span class="co">#&gt;  [95] GO.db_3.18.0                  RSQLite_2.3.3                </span></span>
+<span><span class="co">#&gt;  [97] utf8_1.2.4                    tidyr_1.3.0                  </span></span>
+<span><span class="co">#&gt;  [99] generics_0.1.3                data.table_1.14.8            </span></span>
+<span><span class="co">#&gt; [101] htmlwidgets_1.6.3             prettyunits_1.2.0            </span></span>
+<span><span class="co">#&gt; [103] graphlayouts_1.0.2            httr_1.4.7                   </span></span>
+<span><span class="co">#&gt; [105] S4Arrays_1.3.1                scatterpie_0.2.1             </span></span>
+<span><span class="co">#&gt; [107] pkgconfig_2.0.3               gtable_0.3.4                 </span></span>
+<span><span class="co">#&gt; [109] blob_1.2.4                    XVector_0.43.0               </span></span>
+<span><span class="co">#&gt; [111] shadowtext_0.1.2              clusterProfiler_4.11.0       </span></span>
+<span><span class="co">#&gt; [113] htmltools_0.5.7               carData_3.0-5                </span></span>
+<span><span class="co">#&gt; [115] geneset_0.2.7                 profvis_0.3.8                </span></span>
+<span><span class="co">#&gt; [117] bookdown_0.36                 fgsea_1.29.0                 </span></span>
+<span><span class="co">#&gt; [119] scales_1.3.0                  Biobase_2.63.0               </span></span>
+<span><span class="co">#&gt; [121] png_0.1-8                     ggfun_0.1.3                  </span></span>
+<span><span class="co">#&gt; [123] knitr_1.45                    reshape2_1.4.4               </span></span>
+<span><span class="co">#&gt; [125] nlme_3.1-164                  curl_5.1.0                   </span></span>
+<span><span class="co">#&gt; [127] cachem_1.0.8                  stringr_1.5.1                </span></span>
+<span><span class="co">#&gt; [129] BiocVersion_3.19.1            miniUI_0.1.1.1               </span></span>
+<span><span class="co">#&gt; [131] parallel_4.4.0                HDO.db_0.99.1                </span></span>
+<span><span class="co">#&gt; [133] AnnotationDbi_1.65.2          pillar_1.9.0                 </span></span>
+<span><span class="co">#&gt; [135] grid_4.4.0                    vctrs_0.6.4                  </span></span>
+<span><span class="co">#&gt; [137] urlchecker_1.0.1              promises_1.2.1               </span></span>
+<span><span class="co">#&gt; [139] ggpubr_0.6.0                  car_3.1-2                    </span></span>
+<span><span class="co">#&gt; [141] dbplyr_2.4.0                  xtable_1.8-4                 </span></span>
+<span><span class="co">#&gt; [143] Rgraphviz_2.47.0              evaluate_0.23                </span></span>
+<span><span class="co">#&gt; [145] cli_3.6.1                     locfit_1.5-9.8               </span></span>
+<span><span class="co">#&gt; [147] compiler_4.4.0                rlang_1.1.2                  </span></span>
+<span><span class="co">#&gt; [149] crayon_1.5.2                  ggsignif_0.6.4               </span></span>
+<span><span class="co">#&gt; [151] labeling_0.4.3                ps_1.7.5                     </span></span>
+<span><span class="co">#&gt; [153] plyr_1.8.9                    fs_1.6.3                     </span></span>
+<span><span class="co">#&gt; [155] stringi_1.8.2                 viridisLite_0.4.2            </span></span>
+<span><span class="co">#&gt; [157] BiocParallel_1.37.0           MPO.db_0.99.7                </span></span>
+<span><span class="co">#&gt; [159] munsell_0.5.0                 Biostrings_2.71.1            </span></span>
+<span><span class="co">#&gt; [161] lazyeval_0.2.2                devtools_2.4.5               </span></span>
+<span><span class="co">#&gt; [163] GOSemSim_2.29.0               Matrix_1.6-3                 </span></span>
+<span><span class="co">#&gt; [165] europepmc_0.4.3               hms_1.1.3                    </span></span>
+<span><span class="co">#&gt; [167] patchwork_1.1.3               bit64_4.0.5                  </span></span>
+<span><span class="co">#&gt; [169] ggplot2_3.4.4                 KEGGREST_1.43.0              </span></span>
+<span><span class="co">#&gt; [171] statmod_1.5.0                 shiny_1.8.0                  </span></span>
+<span><span class="co">#&gt; [173] SummarizedExperiment_1.33.1   interactiveDisplayBase_1.41.0</span></span>
+<span><span class="co">#&gt; [175] highr_0.10                    AnnotationHub_3.11.0         </span></span>
+<span><span class="co">#&gt; [177] igraph_1.5.1                  broom_1.0.5                  </span></span>
+<span><span class="co">#&gt; [179] memoise_2.0.1                 bslib_0.6.1                  </span></span>
+<span><span class="co">#&gt; [181] ggtree_3.11.0                 fastmatch_1.1-4              </span></span>
+<span><span class="co">#&gt; [183] bit_4.0.5                     MonoPoly_0.3-10              </span></span>
+<span><span class="co">#&gt; [185] gson_0.1.0                    ape_5.7-1</span></span></code></pre>
 </div>
 <div class="section level2">
 <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
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diff --git a/articles/index.html b/articles/index.html
index 0286137..f5846f3 100644
--- a/articles/index.html
+++ b/articles/index.html
@@ -61,7 +61,7 @@ <h3>All vignettes</h3>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/authors.html b/authors.html
index dd5f5be..18c99a1 100644
--- a/authors.html
+++ b/authors.html
@@ -82,7 +82,7 @@ <h2 id="citation">Citation</h2>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/index.html b/index.html
index bb42105..70b0cc3 100644
--- a/index.html
+++ b/index.html
@@ -5,15 +5,7 @@
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
 <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
-<meta name="description" content="MIRit is an R package that provides several methods for
-    investigating the relationships between miRNAs and genes in different
-    biological conditions. In particular, MIRit allows to explore the functions
-    of dysregulated miRNAs, and makes it possible to identify miRNA-gene
-    regulatory axes that control biological pathways, thus enabling the users
-    to unveil the complexity of miRNA biology. MIRit is an all-in-one framework
-    that aims to help researchers in all the central aspects of an integrative
-    miRNA-mRNA analyses, from differential expression analysis to network
-    characterization.">
+<meta name="description" content="MIRit is an R package that provides several methods for investigating the relationships between miRNAs and genes in different biological conditions. In particular, MIRit allows to explore the functions of dysregulated miRNAs, and makes it possible to identify miRNA-gene regulatory axes that control biological pathways, thus enabling the users to unveil the complexity of miRNA biology. MIRit is an all-in-one framework that aims to help researchers in all the central aspects of an integrative miRNA-mRNA analyses, from differential expression analysis to network characterization.">
 <title>Integrative miRNA-mRNA analysis with MIRit • MIRit</title>
 <!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="favicon-16x16.png">
 <link rel="icon" type="image/png" sizes="32x32" href="favicon-32x32.png">
@@ -26,15 +18,7 @@
 <script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
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 <!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Integrative miRNA-mRNA analysis with MIRit">
-<meta property="og:description" content="MIRit is an R package that provides several methods for
-    investigating the relationships between miRNAs and genes in different
-    biological conditions. In particular, MIRit allows to explore the functions
-    of dysregulated miRNAs, and makes it possible to identify miRNA-gene
-    regulatory axes that control biological pathways, thus enabling the users
-    to unveil the complexity of miRNA biology. MIRit is an all-in-one framework
-    that aims to help researchers in all the central aspects of an integrative
-    miRNA-mRNA analyses, from differential expression analysis to network
-    characterization.">
+<meta property="og:description" content="MIRit is an R package that provides several methods for investigating the relationships between miRNAs and genes in different biological conditions. In particular, MIRit allows to explore the functions of dysregulated miRNAs, and makes it possible to identify miRNA-gene regulatory axes that control biological pathways, thus enabling the users to unveil the complexity of miRNA biology. MIRit is an all-in-one framework that aims to help researchers in all the central aspects of an integrative miRNA-mRNA analyses, from differential expression analysis to network characterization.">
 <meta property="og:image" content="/logo.svg">
 <!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
@@ -199,7 +183,7 @@ <h2 data-toc-skip>Developers</h2>
 
 <div class="pkgdown-footer-right">
   <p></p>
-<p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer>
diff --git a/news/index.html b/news/index.html
index 3cd50a8..6211c13 100644
--- a/news/index.html
+++ b/news/index.html
@@ -59,7 +59,7 @@ <h2 class="pkg-version" data-toc-text="0.99.0" id="mirit-0990">MIRit 0.99.0<a cl
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/pkgdown.yml b/pkgdown.yml
index 1097853..e673e7b 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -1,7 +1,7 @@
 pandoc: 3.1.1
-pkgdown: 2.0.7
-pkgdown_sha: ~
+pkgdown: 2.0.7.9000
+pkgdown_sha: 13d8d621105a3a527a05e46c6c805a6564c39791
 articles:
   MIRit: MIRit.html
-last_built: 2023-11-30T15:49Z
+last_built: 2023-11-30T16:22Z
 
diff --git a/reference/FunctionalEnrichment-class.html b/reference/FunctionalEnrichment-class.html
index 9eac8d0..fde2b89 100644
--- a/reference/FunctionalEnrichment-class.html
+++ b/reference/FunctionalEnrichment-class.html
@@ -185,7 +185,7 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/IntegrativePathwayAnalysis-class.html b/reference/IntegrativePathwayAnalysis-class.html
index 79bb2a5..249cced 100644
--- a/reference/IntegrativePathwayAnalysis-class.html
+++ b/reference/IntegrativePathwayAnalysis-class.html
@@ -242,7 +242,7 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/MIRit-package.html b/reference/MIRit-package.html
index b1d3b9a..b65299f 100644
--- a/reference/MIRit-package.html
+++ b/reference/MIRit-package.html
@@ -76,7 +76,7 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/MirnaExperiment-class.html b/reference/MirnaExperiment-class.html
index 23fef89..6436bc2 100644
--- a/reference/MirnaExperiment-class.html
+++ b/reference/MirnaExperiment-class.html
@@ -362,7 +362,7 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/MirnaExperiment.html b/reference/MirnaExperiment.html
index 0eb4615..5b4e55d 100644
--- a/reference/MirnaExperiment.html
+++ b/reference/MirnaExperiment.html
@@ -183,7 +183,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
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diff --git a/reference/addDifferentialExpression.html b/reference/addDifferentialExpression.html
index a8a169a..239d393 100644
--- a/reference/addDifferentialExpression.html
+++ b/reference/addDifferentialExpression.html
@@ -271,7 +271,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/augmentedPathways.html b/reference/augmentedPathways.html
index 90b76c5..0d3f580 100644
--- a/reference/augmentedPathways.html
+++ b/reference/augmentedPathways.html
@@ -104,7 +104,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/batchCorrection.html b/reference/batchCorrection.html
index df7e37e..4d1b621 100644
--- a/reference/batchCorrection.html
+++ b/reference/batchCorrection.html
@@ -218,7 +218,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/deAccessors.html b/reference/deAccessors.html
index 82d9df7..4736077 100644
--- a/reference/deAccessors.html
+++ b/reference/deAccessors.html
@@ -3,9 +3,7 @@
 and geneDE slots of MirnaExperiment class,
 respectively. Thus, they can be used to explore the results of miRNA and
 gene differential expression analysis stored in a
-MirnaExperiment object."><title>Extract differentially expressed miRNAs and genes from a
-MirnaExperiment object — deAccessors • MIRit</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Extract differentially expressed miRNAs and genes from a
-MirnaExperiment object — deAccessors"><meta property="og:description" content="The mirnaDE() and geneDE() are two accessor functions for the mirnaDE
+MirnaExperiment object."><title>Extract differentially expressed miRNAs and genes from a MirnaExperiment object — deAccessors • MIRit</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Extract differentially expressed miRNAs and genes from a MirnaExperiment object — deAccessors"><meta property="og:description" content="The mirnaDE() and geneDE() are two accessor functions for the mirnaDE
 and geneDE slots of MirnaExperiment class,
 respectively. Thus, they can be used to explore the results of miRNA and
 gene differential expression analysis stored in a
@@ -53,8 +51,7 @@
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="../logo.svg" class="logo" alt=""><h1>Extract differentially expressed miRNAs and genes from a
-<code><a href="MirnaExperiment-class.html">MirnaExperiment</a></code> object</h1>
+      <img src="../logo.svg" class="logo" alt=""><h1>Extract differentially expressed miRNAs and genes from a <code><a href="MirnaExperiment-class.html">MirnaExperiment</a></code> object</h1>
       <small class="dont-index">Source: <a href="https://github.com/jacopo-ronchi/MIRit/blob/HEAD/R/AllGenerics.R" class="external-link"><code>R/AllGenerics.R</code></a></small>
       <div class="d-none name"><code>deAccessors.Rd</code></div>
     </div>
@@ -140,7 +137,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/deAnalysis.html b/reference/deAnalysis.html
index 7cd0d5e..e476636 100644
--- a/reference/deAnalysis.html
+++ b/reference/deAnalysis.html
@@ -401,7 +401,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
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diff --git a/reference/enrichGenes.html b/reference/enrichGenes.html
index 30c7a1e..385f559 100644
--- a/reference/enrichGenes.html
+++ b/reference/enrichGenes.html
@@ -352,6 +352,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">de_enr</span> <span class="op">&lt;-</span> <span class="fu">enrichGenes</span><span class="op">(</span><span class="va">obj</span>, method <span class="op">=</span> <span class="st">"GSEA"</span>, database <span class="op">=</span> <span class="st">"KEGG"</span><span class="op">)</span></span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> Since not specified, 'category' for KEGG database is set to pathway (default).</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> Preparing the appropriate gene set...</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> Some ID occurs one-to-many match, like "79154, 7920, 79143"...</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> 99.96% genes are mapped to symbol</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> Ranking genes based on signed.pval...</span>
@@ -376,7 +377,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/enrichTargets-1.png b/reference/enrichTargets-1.png
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diff --git a/reference/enrichTargets.html b/reference/enrichTargets.html
index f147fd2..b304307 100644
--- a/reference/enrichTargets.html
+++ b/reference/enrichTargets.html
@@ -216,7 +216,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/enrichedFeatures.html b/reference/enrichedFeatures.html
index 9b7f406..eef129c 100644
--- a/reference/enrichedFeatures.html
+++ b/reference/enrichedFeatures.html
@@ -108,7 +108,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
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diff --git a/reference/enrichmentBarplot.html b/reference/enrichmentBarplot.html
index 31f94ca..0a3cde8 100644
--- a/reference/enrichmentBarplot.html
+++ b/reference/enrichmentBarplot.html
@@ -148,7 +148,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/enrichmentDatabase.html b/reference/enrichmentDatabase.html
index 8fa1167..1876cb1 100644
--- a/reference/enrichmentDatabase.html
+++ b/reference/enrichmentDatabase.html
@@ -105,7 +105,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/enrichmentDotplot-1.png b/reference/enrichmentDotplot-1.png
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diff --git a/reference/enrichmentDotplot.html b/reference/enrichmentDotplot.html
index 4f6cbf0..0dcec35 100644
--- a/reference/enrichmentDotplot.html
+++ b/reference/enrichmentDotplot.html
@@ -149,7 +149,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/enrichmentMethod.html b/reference/enrichmentMethod.html
index 550e926..06c6dba 100644
--- a/reference/enrichmentMethod.html
+++ b/reference/enrichmentMethod.html
@@ -105,7 +105,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/enrichmentMetric.html b/reference/enrichmentMetric.html
index 3c55819..2b4a76d 100644
--- a/reference/enrichmentMetric.html
+++ b/reference/enrichmentMetric.html
@@ -101,7 +101,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/enrichmentResults.html b/reference/enrichmentResults.html
index caad1c9..c486b20 100644
--- a/reference/enrichmentResults.html
+++ b/reference/enrichmentResults.html
@@ -102,7 +102,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/findMirnaSNPs.html b/reference/findMirnaSNPs.html
index 1f762f8..19ed837 100644
--- a/reference/findMirnaSNPs.html
+++ b/reference/findMirnaSNPs.html
@@ -138,7 +138,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co"># search disease</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="searchDisease.html">searchDisease</a></span><span class="op">(</span><span class="st">"Alzheimer disease"</span><span class="op">)</span></span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> Checking for cached EFO traits...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Reading EFO traits from cache...</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> Downloading EFO traits, this may take some minutes...</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> Searching for disease: Alzheimer disease</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Alzheimer's disease biomarker measurement" </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [2] "Alzheimer's disease neuropathologic change"</span>
@@ -152,7 +152,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">association</span> <span class="op">&lt;-</span> <span class="fu">findMirnaSNPs</span><span class="op">(</span><span class="va">obj</span>, <span class="va">disId</span><span class="op">)</span></span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> Querying GWAS Catalog, this may take some time...</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> Finding genomic information of differentially expressed miRNAs...</span>
-<span class="r-err co"><span class="r-pr">#&gt;</span> <span class="error">Error in bmRequest(request = request, httr_config = httr_config, verbose = verbose):</span> Bad Gateway (HTTP 502).</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> No disease-related SNPs are present within DE-miRNA loci.</span>
 <span class="r-in"><span><span class="co"># }</span></span></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
@@ -166,7 +166,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/geneCounts.html b/reference/geneCounts.html
index e4c77e9..c50b48b 100644
--- a/reference/geneCounts.html
+++ b/reference/geneCounts.html
@@ -107,7 +107,7 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/geneSet.html b/reference/geneSet.html
index 28e80f1..8758020 100644
--- a/reference/geneSet.html
+++ b/reference/geneSet.html
@@ -101,7 +101,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/getEvidence.html b/reference/getEvidence.html
index ca338a6..9092243 100644
--- a/reference/getEvidence.html
+++ b/reference/getEvidence.html
@@ -115,7 +115,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
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index 7f8e10a..8a17479 100644
--- a/reference/getTargets.html
+++ b/reference/getTargets.html
@@ -180,9 +180,9 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co"># retrieve targets</span></span></span>
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 <span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Loading miRTarBase from cache...</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> Downloading validated interactions from miRTarBase v9.0...</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> Merging predicted and validated results...</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> 13605 miRNA-target pairs have been identified for the 40 differentially expressed miRNAs.</span>
 <span class="r-in"><span><span class="co"># }</span></span></span>
@@ -201,7 +201,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/gseaPlot-1.png b/reference/gseaPlot-1.png
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diff --git a/reference/gseaPlot.html b/reference/gseaPlot.html
index 909cb26..8dd82b2 100644
--- a/reference/gseaPlot.html
+++ b/reference/gseaPlot.html
@@ -4,9 +4,7 @@
 input GSEA results originating from the enrichGenes() function, and
 returns a ggplot2 object with GSEA plot. In this kind of plots, the
 running enrichment score (ES) for a given pathway is shown on the y-axis,
-whereas gene positions in the ranked list are reported on the x-axis."><title>Create a GSEA plot that displays the running enrichment score (ES) for a
-given pathway — gseaPlot • MIRit</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Create a GSEA plot that displays the running enrichment score (ES) for a
-given pathway — gseaPlot"><meta property="og:description" content="This function creates a classic enrichment plot to show the results of
+whereas gene positions in the ranked list are reported on the x-axis."><title>Create a GSEA plot that displays the running enrichment score (ES) for a given pathway — gseaPlot • MIRit</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Create a GSEA plot that displays the running enrichment score (ES) for a given pathway — gseaPlot"><meta property="og:description" content="This function creates a classic enrichment plot to show the results of
 gene set enrichment analyses (GSEA). In particular, this function takes as
 input GSEA results originating from the enrichGenes() function, and
 returns a ggplot2 object with GSEA plot. In this kind of plots, the
@@ -55,8 +53,7 @@
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="../logo.svg" class="logo" alt=""><h1>Create a GSEA plot that displays the running enrichment score (ES) for a
-given pathway</h1>
+      <img src="../logo.svg" class="logo" alt=""><h1>Create a GSEA plot that displays the running enrichment score (ES) for a given pathway</h1>
       <small class="dont-index">Source: <a href="https://github.com/jacopo-ronchi/MIRit/blob/HEAD/R/visualization.R" class="external-link"><code>R/visualization.R</code></a></small>
       <div class="d-none name"><code>gseaPlot.Rd</code></div>
     </div>
@@ -164,7 +161,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/gseaRidgeplot-1.png b/reference/gseaRidgeplot-1.png
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diff --git a/reference/gseaRidgeplot.html b/reference/gseaRidgeplot.html
index 6e8077f..bd1bed0 100644
--- a/reference/gseaRidgeplot.html
+++ b/reference/gseaRidgeplot.html
@@ -137,7 +137,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/index.html b/reference/index.html
index 3d7b0cd..90e18e6 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -66,7 +66,7 @@ <h2 id="the-mirnaexperiment-class">The <code>MirnaExperiment</code> class<a clas
           
           <code><a href="MirnaExperiment.html">MirnaExperiment()</a></code> 
         </dt>
-        <dd>The constructor function for <a href="https://rdrr.io/pkg/MIRit/man/MirnaExperiment-class.html" class="external-link">MirnaExperiment</a></dd>
+        <dd>The constructor function for MirnaExperiment</dd>
       </dl><dl><dt>
           
           <code><a href="pairedSamples.html">pairedSamples()</a></code> 
@@ -113,8 +113,7 @@ <h2 id="differential-expression-analysis">Differential expression analysis<a cla
           
           <code><a href="plotDimensions.html">plotDimensions()</a></code> 
         </dt>
-        <dd>Generate multidimensional scaling (MDS) plots to explore miRNA/gene
-expression distances</dd>
+        <dd>Generate multidimensional scaling (MDS) plots to explore miRNA/gene expression distances</dd>
       </dl><dl><dt>
           
           <code><a href="deAnalysis.html">performMirnaDE()</a></code> <code><a href="deAnalysis.html">performGeneDE()</a></code> 
@@ -129,8 +128,7 @@ <h2 id="differential-expression-analysis">Differential expression analysis<a cla
           
           <code><a href="deAccessors.html">mirnaDE()</a></code> <code><a href="deAccessors.html">geneDE()</a></code> 
         </dt>
-        <dd>Extract differentially expressed miRNAs and genes from a
-<code><a href="https://rdrr.io/pkg/MIRit/man/MirnaExperiment-class.html" class="external-link">MirnaExperiment</a></code> object</dd>
+        <dd>Extract differentially expressed miRNAs and genes from a <code>MirnaExperiment</code> object</dd>
       </dl><dl><dt>
           
           <code><a href="significantAccessors.html">significantMirnas()</a></code> <code><a href="significantAccessors.html">significantGenes()</a></code> 
@@ -140,8 +138,7 @@ <h2 id="differential-expression-analysis">Differential expression analysis<a cla
           
           <code><a href="plotDE.html">plotDE()</a></code> 
         </dt>
-        <dd>Represent differentially expressed miRNAs/genes as boxplots, barplots or
-violinplots</dd>
+        <dd>Represent differentially expressed miRNAs/genes as boxplots, barplots or violinplots</dd>
       </dl><dl><dt>
           
           <code><a href="plotVolcano.html">plotVolcano()</a></code> 
@@ -217,8 +214,7 @@ <h2 id="functional-enrichment">Functional enrichment<a class="anchor" aria-label
           
           <code><a href="gseaPlot.html">gseaPlot()</a></code> 
         </dt>
-        <dd>Create a GSEA plot that displays the running enrichment score (ES) for a
-given pathway</dd>
+        <dd>Create a GSEA plot that displays the running enrichment score (ES) for a given pathway</dd>
       </dl><dl><dt>
           
           <code><a href="gseaRidgeplot.html">gseaRidgeplot()</a></code> 
@@ -332,8 +328,7 @@ <h2 id="topological-pathway-analysis">Topological pathway analysis<a class="anch
           
           <code><a href="listPathways.html">listPathways()</a></code> 
         </dt>
-        <dd>List all the available biological pathways in KEGG, Reactome and
-WikiPathways</dd>
+        <dd>List all the available biological pathways in KEGG, Reactome and WikiPathways</dd>
       </dl><dl><dt>
           
           <code><a href="preparePathways.html">preparePathways()</a></code> 
@@ -379,7 +374,7 @@ <h2 id="topological-pathway-analysis">Topological pathway analysis<a class="anch
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/integratedPathways.html b/reference/integratedPathways.html
index 95f526a..420d8f2 100644
--- a/reference/integratedPathways.html
+++ b/reference/integratedPathways.html
@@ -105,7 +105,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/integration.html b/reference/integration.html
index 6dc7145..8398070 100644
--- a/reference/integration.html
+++ b/reference/integration.html
@@ -129,7 +129,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/integrationDatabase.html b/reference/integrationDatabase.html
index 184bc9e..b97b95a 100644
--- a/reference/integrationDatabase.html
+++ b/reference/integrationDatabase.html
@@ -106,7 +106,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
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diff --git a/reference/integrationDotplot.html b/reference/integrationDotplot.html
index 45e96b1..be8db52 100644
--- a/reference/integrationDotplot.html
+++ b/reference/integrationDotplot.html
@@ -169,7 +169,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/listPathways.html b/reference/listPathways.html
index 68b50e1..a91ff2a 100644
--- a/reference/listPathways.html
+++ b/reference/listPathways.html
@@ -1,8 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This function can be used to retrieve a list of valid biological pathways
-present in KEGG, Reactome and WikiPathways."><title>List all the available biological pathways in KEGG, Reactome and
-WikiPathways — listPathways • MIRit</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="List all the available biological pathways in KEGG, Reactome and
-WikiPathways — listPathways"><meta property="og:description" content="This function can be used to retrieve a list of valid biological pathways
+present in KEGG, Reactome and WikiPathways."><title>List all the available biological pathways in KEGG, Reactome and WikiPathways — listPathways • MIRit</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="List all the available biological pathways in KEGG, Reactome and WikiPathways — listPathways"><meta property="og:description" content="This function can be used to retrieve a list of valid biological pathways
 present in KEGG, Reactome and WikiPathways."><meta property="og:image" content="/logo.svg"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
@@ -47,8 +45,7 @@
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="../logo.svg" class="logo" alt=""><h1>List all the available biological pathways in KEGG, Reactome and
-WikiPathways</h1>
+      <img src="../logo.svg" class="logo" alt=""><h1>List all the available biological pathways in KEGG, Reactome and WikiPathways</h1>
       <small class="dont-index">Source: <a href="https://github.com/jacopo-ronchi/MIRit/blob/HEAD/R/utils.R" class="external-link"><code>R/utils.R</code></a></small>
       <div class="d-none name"><code>listPathways.Rd</code></div>
     </div>
@@ -215,50 +212,55 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [109] "Omega-9 fatty acid synthesis"                                                    </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [110] "Oxidative stress and redox pathway"                                              </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [111] "Circulating monocytes and cardiac macrophages in diastolic dysfunction"          </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [112] "Osteoclast signaling"                                                            </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [113] "Inflammatory response pathway"                                                   </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [114] "Blood clotting cascade"                                                          </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [115] "Lipids measured in liver metastasis from breast cancer"                          </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [116] "Sphingolipid metabolism (integrated pathway)"                                    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [117] "Regulation of Pgc1a expression by a Gsk3b-Tfeb signaling axis in skeletal muscle"</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [118] "GDNF/RET signaling axis"                                                         </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [119] "Peroxiredoxin 2 induced ovarian failure"                                         </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [120] "Mapk signaling pathway"                                                          </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [121] "Deregulation of renin-angiotensin system by SARS-CoV infection"                  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [122] "Hypoxia-dependent self-renewal of myoblasts"                                     </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [123] "Hypoxia-dependent proliferation of myoblasts"                                    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [124] "Hypoxia-dependent differentiation of myoblasts"                                  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [125] "Burn wound healing"                                                              </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [126] "Fibrin complement receptor 3 signaling pathway"                                  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [127] "Proteasome degradation"                                                          </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [128] "Biogenic amine synthesis"                                                        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [129] "Regulation of actin cytoskeleton"                                                </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [130] "Synthesis and degradation of ketone bodies"                                      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [131] "Exercise-induced circadian regulation"                                           </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [132] "Steroid biosynthesis"                                                            </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [133] "Calcium regulation in cardiac cells"                                             </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [134] "Signal transduction of S1P receptor"                                             </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [135] "FAS pathway and stress induction of HSP regulation"                              </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [136] "Leptin-insulin signaling overlap"                                                </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [137] "Integrin-mediated cell adhesion"                                                 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [138] "miR-1 in cardiac development"                                                    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [139] "Pentose phosphate pathway"                                                       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [140] "Insulin signaling"                                                               </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [141] "Amino acid metabolism"                                                           </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [142] "Leptin and adiponectin"                                                          </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [143] "Wnt signaling pathway and pluripotency"                                          </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [144] "Glutathione and one-carbon metabolism"                                           </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [145] "Focal adhesion"                                                                  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [146] "Toll-like receptor signaling"                                                    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [147] "Oxidative phosphorylation"                                                       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [148] "Arachidonate epoxygenase / epoxide hydrolase"                                    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [149] "Metapathway biotransformation"                                                   </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [150] "Fatty acid beta-oxidation"                                                       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [151] "Fatty acid biosynthesis"                                                         </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [152] "Tryptophan metabolism"                                                           </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [153] "Nucleotide GPCRs"                                                                </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [154] "GPCRs, small ligand"                                                             </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [155] "Monoamine GPCRs"                                                                 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [112] "Elongation of (very) long chain fatty acids"                                     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [113] "Osteoclast signaling"                                                            </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [114] "Inflammatory response pathway"                                                   </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [115] "Blood clotting cascade"                                                          </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [116] "Lipids measured in liver metastasis from breast cancer"                          </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [117] "Sphingolipid metabolism (integrated pathway)"                                    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [118] "Regulation of Pgc1a expression by a Gsk3b-Tfeb signaling axis in skeletal muscle"</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [119] "GDNF/RET signaling axis"                                                         </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [120] "Peroxiredoxin 2 induced ovarian failure"                                         </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [121] "Mapk signaling pathway"                                                          </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [122] "Deregulation of renin-angiotensin system by SARS-CoV infection"                  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [123] "Hypoxia-dependent self-renewal of myoblasts"                                     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [124] "Hypoxia-dependent proliferation of myoblasts"                                    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [125] "Hypoxia-dependent differentiation of myoblasts"                                  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [126] "Burn wound healing"                                                              </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [127] "Fibrin complement receptor 3 signaling pathway"                                  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [128] "Proteasome degradation"                                                          </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [129] "Biogenic amine synthesis"                                                        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [130] "Regulation of actin cytoskeleton"                                                </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [131] "Lac-Phe pathway"                                                                 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [132] "Comprehensive IL-17A signaling"                                                  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [133] "Dravet syndrome: Scn1a-A1783V point mutation model"                              </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [134] "Globo series sphingolipid metabolism"                                            </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [135] "Synthesis and degradation of ketone bodies"                                      </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [136] "Exercise-induced circadian regulation"                                           </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [137] "Steroid biosynthesis"                                                            </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [138] "Calcium regulation in cardiac cells"                                             </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [139] "Signal transduction of S1P receptor"                                             </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [140] "FAS pathway and stress induction of HSP regulation"                              </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [141] "Leptin-insulin signaling overlap"                                                </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [142] "Integrin-mediated cell adhesion"                                                 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [143] "miR-1 in cardiac development"                                                    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [144] "Pentose phosphate pathway"                                                       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [145] "Insulin signaling"                                                               </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [146] "Amino acid metabolism"                                                           </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [147] "Leptin and adiponectin"                                                          </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [148] "Wnt signaling pathway and pluripotency"                                          </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [149] "Glutathione and one-carbon metabolism"                                           </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [150] "Focal adhesion"                                                                  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [151] "Toll-like receptor signaling"                                                    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [152] "Oxidative phosphorylation"                                                       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [153] "Arachidonate epoxygenase / epoxide hydrolase"                                    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [154] "Metapathway biotransformation"                                                   </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [155] "Fatty acid beta-oxidation"                                                       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [156] "Fatty acid biosynthesis"                                                         </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [157] "Tryptophan metabolism"                                                           </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [158] "Nucleotide GPCRs"                                                                </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [159] "GPCRs, small ligand"                                                             </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [160] "Monoamine GPCRs"                                                                 </span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
@@ -271,7 +273,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/loadExamples.html b/reference/loadExamples.html
index 3a4ea92..5695097 100644
--- a/reference/loadExamples.html
+++ b/reference/loadExamples.html
@@ -122,7 +122,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/mirVariantPlot.html b/reference/mirVariantPlot.html
index af91ed6..0d94e92 100644
--- a/reference/mirVariantPlot.html
+++ b/reference/mirVariantPlot.html
@@ -182,7 +182,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/mirnaCounts.html b/reference/mirnaCounts.html
index e13fbc2..434ff3a 100644
--- a/reference/mirnaCounts.html
+++ b/reference/mirnaCounts.html
@@ -107,7 +107,7 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/mirnaIntegration.html b/reference/mirnaIntegration.html
index 2c9c44a..d6ff064 100644
--- a/reference/mirnaIntegration.html
+++ b/reference/mirnaIntegration.html
@@ -263,7 +263,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/mirnaTargets.html b/reference/mirnaTargets.html
index 558f8ae..590e3ce 100644
--- a/reference/mirnaTargets.html
+++ b/reference/mirnaTargets.html
@@ -107,7 +107,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/pairedSamples.html b/reference/pairedSamples.html
index 237df0d..0548841 100644
--- a/reference/pairedSamples.html
+++ b/reference/pairedSamples.html
@@ -108,7 +108,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
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diff --git a/reference/plotCorrelation.html b/reference/plotCorrelation.html
index 897f2d3..1fe00a3 100644
--- a/reference/plotCorrelation.html
+++ b/reference/plotCorrelation.html
@@ -245,7 +245,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/plotDE.html b/reference/plotDE.html
index 11fed30..e79fff3 100644
--- a/reference/plotDE.html
+++ b/reference/plotDE.html
@@ -3,9 +3,7 @@
 useful to visualize miRNA and gene differential expression. The user just
 has to provide a vector of interesting miRNA/genes that he wants to plot
 (e.g. "hsa-miR-34a-5p", "hsa-miR-146b-5p", "PAX8"). The chart type can be
-specified through the graph parameter.'><title>Represent differentially expressed miRNAs/genes as boxplots, barplots or
-violinplots — plotDE • MIRit</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Represent differentially expressed miRNAs/genes as boxplots, barplots or
-violinplots — plotDE"><meta property="og:description" content='This function is able to produce boxplots, barplots and violinplots that are
+specified through the graph parameter.'><title>Represent differentially expressed miRNAs/genes as boxplots, barplots or violinplots — plotDE • MIRit</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Represent differentially expressed miRNAs/genes as boxplots, barplots or violinplots — plotDE"><meta property="og:description" content='This function is able to produce boxplots, barplots and violinplots that are
 useful to visualize miRNA and gene differential expression. The user just
 has to provide a vector of interesting miRNA/genes that he wants to plot
 (e.g. "hsa-miR-34a-5p", "hsa-miR-146b-5p", "PAX8"). The chart type can be
@@ -53,8 +51,7 @@
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="../logo.svg" class="logo" alt=""><h1>Represent differentially expressed miRNAs/genes as boxplots, barplots or
-violinplots</h1>
+      <img src="../logo.svg" class="logo" alt=""><h1>Represent differentially expressed miRNAs/genes as boxplots, barplots or violinplots</h1>
       <small class="dont-index">Source: <a href="https://github.com/jacopo-ronchi/MIRit/blob/HEAD/R/visualization.R" class="external-link"><code>R/visualization.R</code></a></small>
       <div class="d-none name"><code>plotDE.Rd</code></div>
     </div>
@@ -241,7 +238,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/plotDimensions-1.png b/reference/plotDimensions-1.png
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diff --git a/reference/plotDimensions.html b/reference/plotDimensions.html
index e957721..9cda223 100644
--- a/reference/plotDimensions.html
+++ b/reference/plotDimensions.html
@@ -5,9 +5,7 @@
 object, this functions allows to visualize both miRNA and gene expression in
 the multidimensional space. Moreover, it is possible to color samples on the
 basis of specific variables, and this is extremely useful to assess
-miRNA/gene expression variations between distinct biological groups."><title>Generate multidimensional scaling (MDS) plots to explore miRNA/gene
-expression distances — plotDimensions • MIRit</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Generate multidimensional scaling (MDS) plots to explore miRNA/gene
-expression distances — plotDimensions"><meta property="og:description" content="This function performs multidimensional scaling in order to produce a simple
+miRNA/gene expression variations between distinct biological groups."><title>Generate multidimensional scaling (MDS) plots to explore miRNA/gene expression distances — plotDimensions • MIRit</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Generate multidimensional scaling (MDS) plots to explore miRNA/gene expression distances — plotDimensions"><meta property="og:description" content="This function performs multidimensional scaling in order to produce a simple
 scatterplot that shows miRNA/gene expression variations among samples. In
 particular, starting from a MirnaExperiment
 object, this functions allows to visualize both miRNA and gene expression in
@@ -57,8 +55,7 @@
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="../logo.svg" class="logo" alt=""><h1>Generate multidimensional scaling (MDS) plots to explore miRNA/gene
-expression distances</h1>
+      <img src="../logo.svg" class="logo" alt=""><h1>Generate multidimensional scaling (MDS) plots to explore miRNA/gene expression distances</h1>
       <small class="dont-index">Source: <a href="https://github.com/jacopo-ronchi/MIRit/blob/HEAD/R/visualization.R" class="external-link"><code>R/visualization.R</code></a></small>
       <div class="d-none name"><code>plotDimensions.Rd</code></div>
     </div>
@@ -232,7 +229,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
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diff --git a/reference/plotVolcano.html b/reference/plotVolcano.html
index c268839..0e263e4 100644
--- a/reference/plotVolcano.html
+++ b/reference/plotVolcano.html
@@ -220,7 +220,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/preparePathways-1.png b/reference/preparePathways-1.png
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diff --git a/reference/preparePathways.html b/reference/preparePathways.html
index 0ee6fe2..f5077cb 100644
--- a/reference/preparePathways.html
+++ b/reference/preparePathways.html
@@ -168,7 +168,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span> Downloading pathways from KEGG database...</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> Converting identifiers to gene symbols...</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> Adding miRNA-gene interactions to biological pathways...</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>155 pathways have been ignored because they contain too few nodes with gene expression measurement.</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>152 pathways have been ignored because they contain too few nodes with gene expression measurement.</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> Performing topological sorting of pathway nodes...</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># perform the integrative pathway analysis with 1000 permutations</span></span></span>
@@ -185,8 +185,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> Thyroid hormone synthesis Thyroid hormone synthesis 0.3469388 12.12941</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Thyroid cancer                       Thyroid cancer 0.2820513 11.56291</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                           normalized.score       P.Val adj.P.Val</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Thyroid hormone synthesis         8.287455 0.000999001     0.017</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Thyroid cancer                    7.396507 0.000999001     0.042</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Thyroid hormone synthesis         8.418675 0.000999001     0.017</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Thyroid cancer                    7.214414 0.000999001     0.042</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># create a dotplot of integrated pathways</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="integrationDotplot.html">integrationDotplot</a></span><span class="op">(</span><span class="va">ipa</span><span class="op">)</span></span></span>
@@ -208,7 +208,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/searchDisease.html b/reference/searchDisease.html
index 8145d32..59ce503 100644
--- a/reference/searchDisease.html
+++ b/reference/searchDisease.html
@@ -119,7 +119,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/significantAccessors.html b/reference/significantAccessors.html
index 12a3adc..bb3afbb 100644
--- a/reference/significantAccessors.html
+++ b/reference/significantAccessors.html
@@ -118,7 +118,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/supportedOrganisms.html b/reference/supportedOrganisms.html
index 37f492a..6229e32 100644
--- a/reference/supportedOrganisms.html
+++ b/reference/supportedOrganisms.html
@@ -193,7 +193,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/reference/topologicalAnalysis-1.png b/reference/topologicalAnalysis-1.png
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diff --git a/reference/topologicalAnalysis.html b/reference/topologicalAnalysis.html
index 9672040..153f47c 100644
--- a/reference/topologicalAnalysis.html
+++ b/reference/topologicalAnalysis.html
@@ -262,7 +262,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span> Downloading pathways from KEGG database...</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> Converting identifiers to gene symbols...</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> Adding miRNA-gene interactions to biological pathways...</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>155 pathways have been ignored because they contain too few nodes with gene expression measurement.</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>152 pathways have been ignored because they contain too few nodes with gene expression measurement.</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> Performing topological sorting of pathway nodes...</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># perform the integrative pathway analysis with 1000 permutations</span></span></span>
@@ -271,14 +271,16 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span> Generating random permutations...</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating p-values with 1000 permutations...</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> Correcting p-values through the max-T procedure...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> The topologically-aware integrative pathway analysis reported 1 significantly altered pathways!</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> The topologically-aware integrative pathway analysis reported 2 significantly altered pathways!</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># access the results of pathway analysis</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="integratedPathways.html">integratedPathways</a></span><span class="op">(</span><span class="va">ipa</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                             pathway  coverage    score</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Thyroid hormone synthesis Thyroid hormone synthesis 0.3469388 12.12941</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Thyroid cancer                       Thyroid cancer 0.2820513 11.56291</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                           normalized.score       P.Val adj.P.Val</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Thyroid hormone synthesis         7.657364 0.000999001     0.028</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Thyroid hormone synthesis         8.409300 0.000999001     0.020</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Thyroid cancer                    7.845217 0.001998002     0.033</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># create a dotplot of integrated pathways</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="integrationDotplot.html">integrationDotplot</a></span><span class="op">(</span><span class="va">ipa</span><span class="op">)</span></span></span>
@@ -300,7 +302,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
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diff --git a/reference/visualizeNetwork.html b/reference/visualizeNetwork.html
index c284611..c7dffc5 100644
--- a/reference/visualizeNetwork.html
+++ b/reference/visualizeNetwork.html
@@ -315,7 +315,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>MIRit is provided without <strong>any warranty</strong>. Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
 </div>
 
     </footer></div>
diff --git a/search.json b/search.json
index b157033..17da784 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
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See guide create great issue advice.","code":""},{"path":"/CONTRIBUTING.html","id":"pull-request-process","dir":"","previous_headings":"Bigger changes","what":"Pull request process","title":"Contributing to MIRit","text":"Fork package clone onto computer. haven’t done , recommend using usethis::create_from_github(\"jacopo-ronchi/MIRit\", fork = TRUE). Install development dependencies devtools::install_dev_deps(), make sure package passes R CMD check running devtools::check(). R CMD check doesn’t pass cleanly, ’s good idea ask help continuing. Create Git branch pull request (PR). recommend using usethis::pr_init(\"brief-description--change\"). Make changes, commit git, create PR running usethis::pr_push(), following prompts browser. title PR briefly describe change. body PR contain Fixes #issue-number. user-facing changes, add bullet top NEWS.md (.e. just first header). 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General R questions can posed StackOverflow RStudio Community website especially pertain tidyverse RStudio GUI related products.","code":""},{"path":"/SUPPORT.html","id":"issues-or-feature-requests","dir":"","previous_headings":"","what":"Issues or Feature Requests","title":"Getting help with MIRit","text":"opening new issue feature request, sure search issues pull requests ensure one already exist implemented development version. Note. can remove :open search term issues page search open closed issues. See link learn modifying search.","code":""},{"path":"/SUPPORT.html","id":"what-happens-next","dir":"","previous_headings":"","what":"What happens next?","title":"Getting help with MIRit","text":"Bioconductor maintainers limited resources strive responsive possible. Please forget tag appropriate maintainer issue GitHub username (e.g., @username). order make easy possible Bioconductor core developers remediate issue. Provide accurate, brief, reproducible report outlined issue templates. Thank trusting Bioconductor.","code":""},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Jacopo Ronchi. Author, maintainer. Maria Foti. Funder.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Ronchi J Foti M. 'MIRit: integrative R framework identification impaired miRNA-mRNA regulatory networks complex diseases'. bioRxiv (2023). doi:10.1101/2023.11.24.568528","code":"@Article{,   title = {MIRit: an integrative R framework for the identification of impaired miRNA-mRNA regulatory networks in complex diseases},   author = {Jacopo Ronchi and Maria Foti},   year = {2023},   journal = {bioRxiv},   doi = {10.1101/2023.11.24.568528},   url = {https://doi.org/10.1101/2023.11.24.568528}, }"},{"path":"/index.html","id":"mirit-","dir":"","previous_headings":"","what":"Integrative miRNA-mRNA analysis with MIRit","title":"Integrative miRNA-mRNA analysis with MIRit","text":"MIRit (miRNA integration tool) open-source R package aims facilitate comprehension microRNA (miRNA) biology integrative analysis gene miRNA expression data deriving different platforms, including microarrays, RNA-Seq, miRNA-Seq, proteomics single-cell transcriptomics. Given regulatory importance, complete characterization miRNA dysregulations results crucial explore molecular networks may lead insurgence complex diseases. purpose, developed MIRit, --one framework provides flexible powerful methods performing integrative miRNA-mRNA multi-omic analyses start finish.","code":""},{"path":"/index.html","id":"authors","dir":"","previous_headings":"","what":"Authors","title":"Integrative miRNA-mRNA analysis with MIRit","text":"Dr. Jacopo Ronchi 1 (author maintainer) Dr. Maria Foti 1 1School Medicine Surgery, University Milano-Bicocca, Italy","code":""},{"path":"/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Integrative miRNA-mRNA analysis with MIRit","text":"Get latest stable R release CRAN. install MIRit Bioconductor using following code: Alternatively, development version MIRit can installed GitHub :","code":"if (!requireNamespace(\"BiocManager\", quietly = TRUE)) {     install.packages(\"BiocManager\") }  BiocManager::install(\"MIRit\") BiocManager::install(\"jacopo-ronchi/MIRit\")"},{"path":"/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Integrative miRNA-mRNA analysis with MIRit","text":"detailed instructions use MIRit integrative miRNA-mRNA analysis, please refer package vignette Bioconductor. Alternatively, can refer documentation website.","code":""},{"path":"/index.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Integrative miRNA-mRNA analysis with MIRit","text":"use MIRit published research, please cite corresponding paper: Ronchi J Foti M. ‘MIRit: integrative R framework identification impaired miRNA-mRNA regulatory networks complex diseases’. bioRxiv (2023). doi:10.1101/2023.11.24.568528 Please note MIRit package made possible thanks many R bioinformatics software authors, cited either vignettes /paper(s) describing package.","code":""},{"path":"/index.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Integrative miRNA-mRNA analysis with MIRit","text":"Please note MIRit project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":"/index.html","id":"development-tools","dir":"","previous_headings":"","what":"Development tools","title":"Integrative miRNA-mRNA analysis with MIRit","text":"Continuous code testing possible thanks GitHub actions usethis, remotes, rcmdcheck customized use Bioconductor’s docker containers BiocCheck. Code coverage assessment possible thanks codecov covr. documentation website automatically updated thanks pkgdown. code styled automatically thanks styler. documentation formatted thanks devtools roxygen2. details, check dev directory. package developed using biocthis.","code":""},{"path":"/reference/FunctionalEnrichment-class.html","id":null,"dir":"Reference","previous_headings":"","what":"The FunctionalEnrichment class — FunctionalEnrichment-class","title":"The FunctionalEnrichment class — FunctionalEnrichment-class","text":"class introduces possibility store results functional enrichment analyses -representation analysis (ORA), gene set enrichment analysis (GSEA), competitive gene set test accounting inter-gene correlation (CAMERA). different slots contained class used store enrichment results generated enrichGenes() function.","code":""},{"path":"/reference/FunctionalEnrichment-class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The FunctionalEnrichment class — FunctionalEnrichment-class","text":"","code":"# S4 method for FunctionalEnrichment enrichmentResults(object)  # S4 method for FunctionalEnrichment enrichmentDatabase(object)  # S4 method for FunctionalEnrichment enrichmentMethod(object)  # S4 method for FunctionalEnrichment geneSet(object)  # S4 method for FunctionalEnrichment enrichmentMetric(object)  # S4 method for FunctionalEnrichment enrichedFeatures(object)  # S4 method for FunctionalEnrichment show(object)"},{"path":"/reference/FunctionalEnrichment-class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The FunctionalEnrichment class — FunctionalEnrichment-class","text":"object object class FunctionalEnrichment containing enrichment results","code":""},{"path":"/reference/FunctionalEnrichment-class.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"The FunctionalEnrichment class — FunctionalEnrichment-class","text":"enrichmentResults(FunctionalEnrichment): Access data slot take closer look enriched terms enrichment analysis enrichmentDatabase(FunctionalEnrichment): See database used functional enrichment enrichmentMethod(FunctionalEnrichment): Visualize approach used functional enrichment analysis geneSet(FunctionalEnrichment): Access geneSet slot see collection gene sets used GSEA enrichmentMetric(FunctionalEnrichment): View ranking metric used GSEA enrichedFeatures(FunctionalEnrichment): View names pre-ranked features used GSEA show(FunctionalEnrichment): Show method objects class FunctionalEnrichment","code":""},{"path":"/reference/FunctionalEnrichment-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"The FunctionalEnrichment class — FunctionalEnrichment-class","text":"data data.frame object holding output enrichment analysis method method used perform functional enrichment analysis (e.g. Gene Set Enrichment Analysis (GSEA)) organism name organism consideration (e.g. Homo sapiens) database name database used enrichment analysis (e.g. KEGG) pCutoff numeric value defining threshold used statistical significance enrichment analysis (e.g. 0.05) pAdjustment character indicating method used correct p-values multiple testing (e.g. fdr) features character vector containing list features used enrichment statistic numeric vector containing statistic used run GSEA. parameter empty ORA CAMERA universe background universe features. Typically, equal complete list features assayed. slot NULL GSEA geneSet gene set used functional enrichment analysis. list object element contains list genes belonging specific pathway.","code":""},{"path":"/reference/FunctionalEnrichment-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The FunctionalEnrichment class — FunctionalEnrichment-class","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/IntegrativePathwayAnalysis-class.html","id":null,"dir":"Reference","previous_headings":"","what":"The IntegrativePathwayAnalysis class — IntegrativePathwayAnalysis-class","title":"The IntegrativePathwayAnalysis class — IntegrativePathwayAnalysis-class","text":"class stores output integrative multi-omic pathway analyses. particular, slots class suitable represent results topologically-aware integrative pathway analysis (TAIPA) returned topologicalAnalysis() function.","code":""},{"path":"/reference/IntegrativePathwayAnalysis-class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The IntegrativePathwayAnalysis class — IntegrativePathwayAnalysis-class","text":"","code":"# S4 method for IntegrativePathwayAnalysis integratedPathways(object)  # S4 method for IntegrativePathwayAnalysis integrationDatabase(object)  # S4 method for IntegrativePathwayAnalysis augmentedPathways(object)  # S4 method for IntegrativePathwayAnalysis show(object)"},{"path":"/reference/IntegrativePathwayAnalysis-class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The IntegrativePathwayAnalysis class — IntegrativePathwayAnalysis-class","text":"object object class IntegrativePathwayAnalysis containing results miRNA-mRNA pathway analysis","code":""},{"path":[]},{"path":"/reference/IntegrativePathwayAnalysis-class.html","id":"analysis-results","dir":"Reference","previous_headings":"","what":"Analysis results","title":"The IntegrativePathwayAnalysis class — IntegrativePathwayAnalysis-class","text":"data slot class consists data.frame object six columns, namely: pathway, indicates name biological network; coverage, specifies fraction nodes expression measurement available; score, expresses score individual pathway; normalized.score, indicates pathway scores standardizing values null distribution computed permutations; P.Val, resulting p-value pathway; adj.P.Val, p-value adjusted multiple testing.","code":""},{"path":"/reference/IntegrativePathwayAnalysis-class.html","id":"organisms-and-databases","dir":"Reference","previous_headings":"","what":"Organisms and databases","title":"The IntegrativePathwayAnalysis class — IntegrativePathwayAnalysis-class","text":"organism database slots specify organism study database used retrieving biological interactions, respectively. particular, topologicalAnalysis() function supports KEGG, WikiPathways, Reactome databases. Regarding organisms, supportedOrganisms() function can used retrieve available species database.","code":""},{"path":"/reference/IntegrativePathwayAnalysis-class.html","id":"statistical-significance-of-the-permutation-test","dir":"Reference","previous_headings":"","what":"Statistical significance of the permutation test","title":"The IntegrativePathwayAnalysis class — IntegrativePathwayAnalysis-class","text":"pCutoff pAdjustment slots refer cutoff used analysis. pCutoff threshold used defining statistically significant pathways, whereas pAdjustment refers multiple testing correction method used. Furthermore, since statistical significance pathway defined basis permutation test, number permutations also specified nPerm slot.","code":""},{"path":"/reference/IntegrativePathwayAnalysis-class.html","id":"augmented-pathways","dir":"Reference","previous_headings":"","what":"Augmented pathways","title":"The IntegrativePathwayAnalysis class — IntegrativePathwayAnalysis-class","text":"pathways slot contains list weighted graph objects, representing biological pathway. networks enlarged adding observed miRNA-mRNA interactions. network processed weight edge +1 activation interactions, -1 repression interactions, occurring miRNAs mRNAs.","code":""},{"path":"/reference/IntegrativePathwayAnalysis-class.html","id":"differential-expression-results-for-both-mirnas-and-genes","dir":"Reference","previous_headings":"","what":"Differential expression results for both miRNAs and genes","title":"The IntegrativePathwayAnalysis class — IntegrativePathwayAnalysis-class","text":"expression variation miRNAs genes measured study stored expression slot. particular, slot consists data.frame object different information, including log2 fold changes, node weights p-values.","code":""},{"path":"/reference/IntegrativePathwayAnalysis-class.html","id":"minimum-percentage-of-measured-features","dir":"Reference","previous_headings":"","what":"Minimum percentage of measured features","title":"The IntegrativePathwayAnalysis class — IntegrativePathwayAnalysis-class","text":"minPc slot indicates minimum percentage miRNAs/mRNAs pathways considered integrative analysis. needed often, differential expression analysis performed, lowly expressed features removed. Therefore, pathways might result significantly affected even 1% nodes perturbed.","code":""},{"path":"/reference/IntegrativePathwayAnalysis-class.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"The IntegrativePathwayAnalysis class — IntegrativePathwayAnalysis-class","text":"integratedPathways(IntegrativePathwayAnalysis): Access results integrative miRNA-mRNA pathway analysis integrationDatabase(IntegrativePathwayAnalysis): View database used integrative pathway analysis augmentedPathways(IntegrativePathwayAnalysis): Extract list biological networks augmented miRNA-mRNA interactions show(IntegrativePathwayAnalysis): Show method objects class IntegrativePathwayAnalysis","code":""},{"path":"/reference/IntegrativePathwayAnalysis-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"The IntegrativePathwayAnalysis class — IntegrativePathwayAnalysis-class","text":"data data.frame object contains results integrative pathway analysis. See details section details method method used analysis organism name organism consideration (e.g. Homo sapiens) database name database used retrieving biological pathways (e.g. KEGG) pCutoff numeric value defining threshold used statistical significance (e.g. 0.05) pAdjustment character indicating method used correct p-values multiple testing (e.g. fdr) pathways list graph objects containing biological networks retrieved database, augmented miRNA-mRNA interactions expression data.frame object containing differential expression results miRNAs genes minPc minimum percentage measured features pathway must considered analysis nPerm number permutation used assessing statistical significance pathway","code":""},{"path":"/reference/IntegrativePathwayAnalysis-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The IntegrativePathwayAnalysis class — IntegrativePathwayAnalysis-class","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/MIRit-package.html","id":null,"dir":"Reference","previous_headings":"","what":"MIRit: Integrate microRNA and gene expression to decipher pathway complexity — MIRit-package","title":"MIRit: Integrate microRNA and gene expression to decipher pathway complexity — MIRit-package","text":"MIRit R package provides several methods investigating relationships miRNAs genes different biological conditions. particular, MIRit allows explore functions dysregulated miRNAs, makes possible identify miRNA-gene regulatory axes control biological pathways, thus enabling users unveil complexity miRNA biology. MIRit --one framework aims help researchers central aspects integrative miRNA-mRNA analyses, differential expression analysis network characterization.","code":""},{"path":[]},{"path":"/reference/MIRit-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"MIRit: Integrate microRNA and gene expression to decipher pathway complexity — MIRit-package","text":"Maintainer: Jacopo Ronchi jacopo.ronchi@unimib.(ORCID)","code":""},{"path":"/reference/MirnaExperiment-class.html","id":null,"dir":"Reference","previous_headings":"","what":"The 'MirnaExperiment' class — MirnaExperiment-class","title":"The 'MirnaExperiment' class — MirnaExperiment-class","text":"class extends MultiAssayExperiment homonym package provide flexibility handling genomic data microRNAs targets, allowing store information microRNA gene expression, differential expression results, microRNA targets miRNA-gene integration analysis. class can used manage genomic data deriving different sources, like RNA-Seq, microarrays mass spectrometry. Moreover, microRNA gene expression levels may originate individuals (paired samples) different subjects (unpaired samples).","code":""},{"path":"/reference/MirnaExperiment-class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The 'MirnaExperiment' class — MirnaExperiment-class","text":"","code":"# S4 method for MirnaExperiment mirnaDE(object, onlySignificant = TRUE, param = FALSE, returnObject = FALSE)  # S4 method for MirnaExperiment geneDE(object, onlySignificant = TRUE, param = FALSE, returnObject = FALSE)  # S4 method for MirnaExperiment significantMirnas(object)  # S4 method for MirnaExperiment significantGenes(object)  # S4 method for MirnaExperiment pairedSamples(object)  # S4 method for MirnaExperiment mirnaTargets(object)  # S4 method for MirnaExperiment integration(object, param = FALSE)  # S4 method for MirnaExperiment show(object)"},{"path":"/reference/MirnaExperiment-class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The 'MirnaExperiment' class — MirnaExperiment-class","text":"object object class MirnaExperiment onlySignificant Logical, TRUE differential expression results returned just statistically significant miRNAs/genes, FALSE full table miRNA/gene differential expression provided. Default TRUE report significant miRNAs/genes param Logical, whether return complete list object parameters used, just results stored data. Default FALSE returnObject Logical, TRUE function return limma/edgeR/DESeq2 object used differential expression analysis","code":""},{"path":"/reference/MirnaExperiment-class.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"The 'MirnaExperiment' class — MirnaExperiment-class","text":"mirnaDE(MirnaExperiment): Access results miRNA differential expression geneDE(MirnaExperiment): Access results gene differential expression significantMirnas(MirnaExperiment): Access names differentially expressed miRNAs significantGenes(MirnaExperiment): Access names differentially expressed genes pairedSamples(MirnaExperiment): Check object derives sample-matched data mirnaTargets(MirnaExperiment): Extract miRNA-targets interactions retrieved differentially expressed miRNAs integration(MirnaExperiment): Access results integrative miRNA-mRNA analysis show(MirnaExperiment): Show method objects class MirnaExperiment","code":""},{"path":"/reference/MirnaExperiment-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"The 'MirnaExperiment' class — MirnaExperiment-class","text":"ExperimentList ExperimentList class object assay dataset colData DataFrame clinical/specimen data available across experiments sampleMap DataFrame translatable identifiers samples participants metadata Additional data describing object drops metadata list dropped information mirnaDE list object containing results miRNA differential expression geneDE list object containing results gene differential expression pairedSamples logical parameter specifies whether miRNA gene expression measurements derive individuals (TRUE) different subjects (FALSE) targets data.frame object containing miRNA-target pairs. slot commonly populated getTargets() function integration list object containing results integration analysis miRNA gene expression values. slot commonly populated mirnaIntegration() function","code":""},{"path":"/reference/MirnaExperiment-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"The 'MirnaExperiment' class — MirnaExperiment-class","text":"create MirnaExperiment object, can use MirnaExperiment() constructor function, allows easily build verify valid object starting miRNA gene expression matrices.","code":""},{"path":"/reference/MirnaExperiment-class.html","id":"experimentlist","dir":"Reference","previous_headings":"","what":"ExperimentList","title":"The 'MirnaExperiment' class — MirnaExperiment-class","text":"ExperimentList slot designed contain results experiment/assay. case, holds miRNA gene expression matrices. contains SimpleList-class.","code":""},{"path":"/reference/MirnaExperiment-class.html","id":"coldata","dir":"Reference","previous_headings":"","what":"colData","title":"The 'MirnaExperiment' class — MirnaExperiment-class","text":"colData slot collection primary specimen data valid across experiments. slot strictly class DataFrame arguments constructor function allow arguments class data.frame subsequently coerced.","code":""},{"path":"/reference/MirnaExperiment-class.html","id":"samplemap","dir":"Reference","previous_headings":"","what":"sampleMap","title":"The 'MirnaExperiment' class — MirnaExperiment-class","text":"sampleMap contains DataFrame translatable identifiers samples participants biological units. standard column names sampleMap \"assay\", \"primary\", \"colname\". Note \"assay\" column factor corresponding names experiment ExperimentList. case names match sampleMap experiments, documented experiments sampleMap take precedence experiments dropped harmonization procedure. constructor function generate sampleMap case provided method may 'safer' alternative creating MultiAssayExperiment (long rownames identical colData, provided). empty sampleMap may produce empty experiments levels \"assay\" factor sampleMap match names ExperimentList.","code":""},{"path":"/reference/MirnaExperiment-class.html","id":"mirnade-and-genede","dir":"Reference","previous_headings":"","what":"mirnaDE and geneDE","title":"The 'MirnaExperiment' class — MirnaExperiment-class","text":"mirnaDE geneDE consist two list objects storing information regarding miRNA gene differential expression, including: data, contains differential expression results data.frame five columns: ID: indicates name miRNA/gene; logFC: indicates fold change feature logarithmic scale; AveExpr: represents average expression miRNA/gene; P.Value: indicates resulting p-value; adj.P.Val: contains p-values adjusted multiple testing. significant, character vector containing names significantly differentially expressed miRNAs/genes passed thresholds; method, specifies procedure used determine differentially expressed miRNAs/gens (eg. \"limma-voom\", \"edgeR\", \"DESeq2\", \"limma\"); group, column name variable (colData) used differential expression analysis; contrast, represents groups compared differential expression analysis (e.g. 'disease-healthy'); design, outlines R formula used fitting model. includes variable interest (group) together eventual covariates (e.g. '~ 0 + disease + sex'); pCutoff, indicates p-value cutoff used DE analysis; pAdjustment, approach used multiple testing correction; logFC, states log2 Fold Change cutoff used DE analysis; deObject, object deriving limma/edgeR/DESeq2, holds additional information regarding data processing. MiRNA differential expression results can accessed mirnaDE() function, additional details see ?mirnaDE. Similarly, gene differential expression results can accessed geneDE() function, additional details see ?geneDE.","code":""},{"path":"/reference/MirnaExperiment-class.html","id":"pairedsamples","dir":"Reference","previous_headings":"","what":"pairedSamples","title":"The 'MirnaExperiment' class — MirnaExperiment-class","text":"already mentioned, pairedSamples must TRUE miRNA gene expression derive subjects, must FALSE case.","code":""},{"path":"/reference/MirnaExperiment-class.html","id":"targets","dir":"Reference","previous_headings":"","what":"targets","title":"The 'MirnaExperiment' class — MirnaExperiment-class","text":"targets data.frame miRNA-target interactions, retrieved getTargets() function.","code":""},{"path":"/reference/MirnaExperiment-class.html","id":"integration","dir":"Reference","previous_headings":"","what":"integration","title":"The 'MirnaExperiment' class — MirnaExperiment-class","text":"Lastly, integration slot contains list object stores results options used performing integrative miRNA-gene analysis. particular, integration contains: data, data.frame object results integrative analysis; method, specifies procedure used perform integrative analysis; pCutoff, indicates p-value cutoff used analysis; pAdjustment, approach used multiple testing correction. Moreover, data differs basis integration strategy used. one-sided association test integration, integration based rotation gene set tests, data.frame seven columns: microRNA: miRNA ID; mirna.direction: fold change direction DE-miRNA (); gene.direction: fold change direction target genes (); DE: represents number differentially expressed targets; targets: represents total number targets miRNA; P.Val: indicates resulting p-value; adj.P.Val: contains test p-values corrected multiple testing; DE.targets: contains list differentially expressed targets whose expression negatively associated miRNA expression. Instead, correlation analysis performed, data six columns: microRNA: miRNA ID; Target: correlated target gene; microRNA.Direction: fold change direction DE-miRNA; Corr.Coeff: value correlation coefficient used; Corr.P.Value: p-value resulting correlation analysis; Corr.Adjusted.P.Val: contains correlation p-values corrected multiple testing. access results integrative analysis, data slot can accessed integration() function.","code":""},{"path":"/reference/MirnaExperiment-class.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"The 'MirnaExperiment' class — MirnaExperiment-class","text":"Marcel Ramos et al. Software Integration Multiomics Experiments Bioconductor. Cancer Research, 2017 November 1; 77(21); e39-42. DOI: 10.1158/0008-5472.CAN-17-0344","code":""},{"path":[]},{"path":"/reference/MirnaExperiment-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The 'MirnaExperiment' class — MirnaExperiment-class","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/MirnaExperiment.html","id":null,"dir":"Reference","previous_headings":"","what":"The constructor function for MirnaExperiment — MirnaExperiment","title":"The constructor function for MirnaExperiment — MirnaExperiment","text":"constructor function allows easily create objects class MirnaExperiment. function requires inputs miRNA gene expression matrices, well sample metadata.","code":""},{"path":"/reference/MirnaExperiment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The constructor function for MirnaExperiment — MirnaExperiment","text":"","code":"MirnaExperiment(mirnaExpr, geneExpr, samplesMetadata, pairedSamples = TRUE)"},{"path":"/reference/MirnaExperiment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The constructor function for MirnaExperiment — MirnaExperiment","text":"mirnaExpr matrix object containing microRNA expression levels. objects coercible matrix also accepted (e.g. data.frame). object must structured specified details section geneExpr matrix object containing gene expression levels. objects coercible matrix also accepted (e.g. data.frame). object must structured specified details section samplesMetadata data.frame object containing information samples used microRNA gene expression profiling. information see details section pairedSamples Logical, whether miRNA gene expression levels derive subjects . Check details section additional instructions. Default TRUE","code":""},{"path":"/reference/MirnaExperiment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The constructor function for MirnaExperiment — MirnaExperiment","text":"valid MirnaExperiment object containing information miRNA gene expression.","code":""},{"path":"/reference/MirnaExperiment.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"The constructor function for MirnaExperiment — MirnaExperiment","text":"function requires data prepared described .","code":""},{"path":"/reference/MirnaExperiment.html","id":"mirnaexpr-and-geneexpr","dir":"Reference","previous_headings":"","what":"mirnaExpr and geneExpr","title":"The constructor function for MirnaExperiment — MirnaExperiment","text":"mirnaExpr geneExpr must matrix objects (objects coercible one) contain miRNA gene expression values, respectively. Rows must represent different miRNAs/genes analyzed columns must represent different samples study. mirnaExpr, row names must contain miRNA names according miRBase nomenclature, whereas geneExpr, row names must contain gene symbols according hgnc nomenclature. values contained objects can derive microarray RNA-Seq experiments. NGS experiments, mirnaExpr geneExpr just un-normalized count matrices. Instead, microarray experiments, data normalized log2 transformed, example RMA algorithm.","code":""},{"path":"/reference/MirnaExperiment.html","id":"samplesmetadata","dir":"Reference","previous_headings":"","what":"samplesMetadata","title":"The constructor function for MirnaExperiment — MirnaExperiment","text":"samplesMetadata must data.frame object containing information samples used miRNA profiling gene expression analysis. Specifically, data.frame must contain: column named primary, specifying identifier sample; column named mirnaCol, containing column names used sample mirnaExpr object; column named geneCol, containing column names used sample geneExpr object; eventual columns define specific sample metadata, disease condition, age, sex ... unpaired samples, NAs can used missing entries mirnaCol/geneCol.","code":""},{"path":"/reference/MirnaExperiment.html","id":"pairedsamples","dir":"Reference","previous_headings":"","what":"pairedSamples","title":"The constructor function for MirnaExperiment — MirnaExperiment","text":"MicroRNA gene expression measurements may derive subjects (.e. samples used generate miRNA gene expression data) different individuals (.e. miRNA expression assayed group samples gene expression retrieved different group samples). pairedSamples logical parameter defines relationship miRNA gene expression measurements. must TRUE data derive individuals, must FALSE data derive different subjects.","code":""},{"path":"/reference/MirnaExperiment.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The constructor function for MirnaExperiment — MirnaExperiment","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/MirnaExperiment.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The constructor function for MirnaExperiment — MirnaExperiment","text":"","code":"# load example data data(geneCounts, package = \"MIRit\") data(mirnaCounts, package = \"MIRit\")  # create samples metadata meta <- data.frame(     \"primary\" = colnames(geneCounts),     \"mirnaCol\" = colnames(mirnaCounts), \"geneCol\" = colnames(geneCounts),     \"disease\" = c(rep(\"PTC\", 8), rep(\"NTH\", 8)),     \"patient\" = c(rep(paste(\"Sample_\", seq(8), sep = \"\"), 2)) )  # create a 'MirnaExperiment' object obj <- MirnaExperiment(     mirnaExpr = mirnaCounts, geneExpr = geneCounts,     samplesMetadata = meta, pairedSamples = TRUE )"},{"path":"/reference/addDifferentialExpression.html","id":null,"dir":"Reference","previous_headings":"","what":"Manually add differential expression results to a MirnaExperiment object — addDifferentialExpression","title":"Manually add differential expression results to a MirnaExperiment object — addDifferentialExpression","text":"function allows add miRNA gene differential expression results MirnaExperiment object. Instead running performMirnaDE() performGeneDE() functions, one allows use differential expression analyses carried ways. Note possible manually add differential expression results just miRNAs just genes. particularly useful order use pipeline implemented MIRit proteomic data expression data deriving different technologies.","code":""},{"path":"/reference/addDifferentialExpression.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Manually add differential expression results to a MirnaExperiment object — addDifferentialExpression","text":"","code":"addDifferentialExpression(   mirnaObj,   mirnaDE = NULL,   geneDE = NULL,   mirna.logFC = 1,   mirna.pCutoff = 0.05,   mirna.pAdjustment = \"fdr\",   gene.logFC = 1,   gene.pCutoff = 0.05,   gene.pAdjustment = \"fdr\" )"},{"path":"/reference/addDifferentialExpression.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Manually add differential expression results to a MirnaExperiment object — addDifferentialExpression","text":"mirnaObj MirnaExperiment object containing miRNA gene data mirnaDE data.frame containing output miRNA differential expression analysis. Check details section see required format. Default NULL add miRNA differential expression results geneDE data.frame containing output gene differential expression analysis. Check details section see required format. Default NULL add gene differential expression results mirna.logFC minimum log2 fold change required consider miRNA differentially expressed. Default 1, retain two-fold differences mirna.pCutoff adjusted p-value cutoff use miRNA statistical significance. default value 0.05 mirna.pAdjustment p-value correction method miRNA multiple testing. must one : fdr (default), BH, none, holm, hochberg, hommel, bonferroni, gene.logFC minimum log2 fold change required consider gene differentially expressed. Default 1, retain two-fold differences gene.pCutoff adjusted p-value cutoff use gene statistical significance. default value 0.05 gene.pAdjustment p-value correction method gene multiple testing. must one : fdr (default), BH, none, holm, hochberg, hommel, bonferroni, ","code":""},{"path":"/reference/addDifferentialExpression.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Manually add differential expression results to a MirnaExperiment object — addDifferentialExpression","text":"MirnaExperiment object containing differential expression results. access results, user may run mirnaDE() geneDE() functions miRNAs genes, respectively.","code":""},{"path":"/reference/addDifferentialExpression.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Manually add differential expression results to a MirnaExperiment object — addDifferentialExpression","text":"following paragraphs briefly explain formats needed mirnaDE, geneDE, differential expression parameters.","code":""},{"path":"/reference/addDifferentialExpression.html","id":"mirnade-and-genede","dir":"Reference","previous_headings":"","what":"mirnaDE and geneDE","title":"Manually add differential expression results to a MirnaExperiment object — addDifferentialExpression","text":"mirnaDE geneDE two objects class data.frame containing results miRNA gene differential expression analysis respectively. tables contain differential expression results miRNAs/genes analyzed, just statistically significant species. Note can individually add differential expression results miRNAs genes. instance, possible manually add gene differential expression function, performing miRNA differential expression performMirnaDE() function, vice versa. order add miRNA gene differential expression results, must leave mirnaDE geneDE slots NULL. data.frame objects can used, long : One column containing miRNA/gene names (according miRBase/hgnc nomenclature). Accepted column names : ID, Symbol, Gene_Symbol, Mirna, mir, Gene, gene.symbol, Gene.symbol; One column log2 fold changes. Accepted column names : logFC, log2FoldChange, FC, lFC; One column average expression. Accepted column names : AveExpr, baseMean, logCPM; One column p-values resulting differential expression analysis. Accepted column names : P.Value, pvalue, PValue, Pvalue; One column containing p-values adjusted multiple testing. Accepted column names : adj.P.Val, padj, FDR, fdr, adj, adj.p, adjp.","code":""},{"path":"/reference/addDifferentialExpression.html","id":"differential-expression-cutoffs","dir":"Reference","previous_headings":"","what":"Differential expression cutoffs","title":"Manually add differential expression results to a MirnaExperiment object — addDifferentialExpression","text":"mirna.logFC, mirna.pCutoff, mirna.pAdjustment, gene.logFC, gene.pCutoff, gene.pAdjustment represent parameters used define significance differential expression results. needed order inform MIRit features considered differentially expressed.","code":""},{"path":"/reference/addDifferentialExpression.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Manually add differential expression results to a MirnaExperiment object — addDifferentialExpression","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/addDifferentialExpression.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Manually add differential expression results to a MirnaExperiment object — addDifferentialExpression","text":"","code":"# load example data data(geneCounts, package = \"MIRit\") data(mirnaCounts, package = \"MIRit\")  # create samples metadata meta <- data.frame(     \"primary\" = colnames(geneCounts),     \"mirnaCol\" = colnames(mirnaCounts), \"geneCol\" = colnames(geneCounts),     \"disease\" = c(rep(\"PTC\", 8), rep(\"NTH\", 8)),     \"patient\" = c(rep(paste(\"Sample_\", seq(8), sep = \"\"), 2)) )  # create a 'MirnaExperiment' object obj <- MirnaExperiment(     mirnaExpr = mirnaCounts, geneExpr = geneCounts,     samplesMetadata = meta, pairedSamples = TRUE )  # perform miRNA DE with DESeq2 separately dds_m <- DESeq2::DESeqDataSetFromMatrix(     countData = mirnaCounts, colData = meta, design = ~ 0 + disease + patient ) #> Warning: some variables in design formula are characters, converting to factors dds_m <- DESeq2::DESeq(dds_m) #> estimating size factors #> estimating dispersions #> gene-wise dispersion estimates #> mean-dispersion relationship #> final dispersion estimates #> fitting model and testing de_m <- as.data.frame(DESeq2::results(dds_m,     contrast = c(\"disease\", \"PTC\", \"NTH\"), pAdjustMethod = \"fdr\" ))  # perform gene DE with DESeq2 separately dds_g <- DESeq2::DESeqDataSetFromMatrix(     countData = geneCounts, colData = meta, design = ~ 0 + disease + patient ) #> Warning: some variables in design formula are characters, converting to factors dds_g <- DESeq2::DESeq(dds_g) #> estimating size factors #> estimating dispersions #> gene-wise dispersion estimates #> mean-dispersion relationship #> final dispersion estimates #> fitting model and testing de_g <- as.data.frame(DESeq2::results(dds_g,     contrast = c(\"disease\", \"PTC\", \"NTH\"), pAdjustMethod = \"fdr\" ))  # prepare DE tables de_m$ID <- rownames(de_m) de_m <- na.omit(de_m) de_g$ID <- rownames(de_g) de_g <- na.omit(de_g)  # add DE results to MirnaExperiment object obj <- addDifferentialExpression(obj, de_m, de_g,     mirna.logFC = 1, mirna.pCutoff = 0.05,     gene.logFC = 1, gene.pCutoff = 0.05 )"},{"path":"/reference/augmentedPathways.html","id":null,"dir":"Reference","previous_headings":"","what":"Access the miRNA-augmented pathways that were used during TAIPA — augmentedPathways","title":"Access the miRNA-augmented pathways that were used during TAIPA — augmentedPathways","text":"function accesses pathways slot FunctionalEnrichment object returns list object augmented pathways considered topologicalAnalysis() function perform integrative analysis.","code":""},{"path":"/reference/augmentedPathways.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access the miRNA-augmented pathways that were used during TAIPA — augmentedPathways","text":"","code":"augmentedPathways(object)"},{"path":"/reference/augmentedPathways.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access the miRNA-augmented pathways that were used during TAIPA — augmentedPathways","text":"object object class IntegrativePathwayAnalysis containing results miRNA-mRNA pathway analysis","code":""},{"path":"/reference/augmentedPathways.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access the miRNA-augmented pathways that were used during TAIPA — augmentedPathways","text":"list object miRNA-augmented biological pathways.","code":""},{"path":"/reference/augmentedPathways.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Access the miRNA-augmented pathways that were used during TAIPA — augmentedPathways","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/augmentedPathways.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access the miRNA-augmented pathways that were used during TAIPA — augmentedPathways","text":"","code":"# load the example IntegrativePathwayAnalysis object obj <- loadExamples(\"IntegrativePathwayAnalysis\")  # extract the pathways ps <- augmentedPathways(obj)"},{"path":"/reference/batchCorrection.html","id":null,"dir":"Reference","previous_headings":"","what":"Correct for batch effects in miRNA and gene expression measurements — batchCorrection","title":"Correct for batch effects in miRNA and gene expression measurements — batchCorrection","text":"function allows remove unwanted batch effects miRNA gene expression matrices. particular, function fits linear model miRNA/gene expression levels, removes variability caused batch effects. Furthermore, weighted surrogate variable analysis (WSVA) can also included remove effects due surrogate variables. batch effects present, crucial remove function moving correlation analysis.","code":""},{"path":"/reference/batchCorrection.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Correct for batch effects in miRNA and gene expression measurements — batchCorrection","text":"","code":"batchCorrection(   mirnaObj,   assay,   batch = NULL,   batch2 = NULL,   covariates = NULL,   includeWsva = FALSE,   n.sv = 1L,   weight.by.sd = TRUE )"},{"path":"/reference/batchCorrection.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Correct for batch effects in miRNA and gene expression measurements — batchCorrection","text":"mirnaObj MirnaExperiment object containing miRNA gene data assay expression matrix correct. must one genes microRNA batch must name variable present colData MirnaExperiment object (eg. \"disease\"), , alternatively, must character/factor object defines batch memberships. See details section additional information batch2 must name variable present colData MirnaExperiment object (eg. \"disease\"), , alternatively, must character/factor object defines another series batches additive effects specified batch. See details section additional information covariates Additional numeric covariates want correct . must character vector containing names numeric variables present colData MirnaExperiment object (eg. c(\"age\", \"RIN\", \"quantity\")), , alternatively, must simple matrix object. See details section additional information includeWsva Logical, whether correct surrogate variables . Default FALSE n.sv number surrogate variables estimate weight..sd Logical, whether specifically tune surrogate variables variable genes . Default TRUE","code":""},{"path":"/reference/batchCorrection.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Correct for batch effects in miRNA and gene expression measurements — batchCorrection","text":"MirnaExperiment object containing batch effect-corrected expression matrices.","code":""},{"path":"/reference/batchCorrection.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Correct for batch effects in miRNA and gene expression measurements — batchCorrection","text":"Batch effects consist unwanted sources technical variation confound expression variability limit downstream analyses. Since reliability biological conclusions integrative miRNA-mRNA analyses depends association miRNA gene expression levels, pivotal ensure expression measurements affected technical variations. regard, batch effects noticed data, user run function using mirnaIntegration() function perform correlation analysis. Usually, given MirnaExperiment object, user specify: assay want remove batch effects (one genes microRNA); batch variable, variable defines different batches; batch2 variable, can included correct second series batches additive effects specified batch; covariates variables, allows correction one continuous numeric effects. particular, batch batch2 provided names covariates included colData MirnaExperiment object. Alternatively, can character/factor objects declare batch memberships. Similarly, covariates can supplied vector containing names numeric variables listed colData MirnaExperiment objects, can provided simple matrix. Additionally, influence unknown sources technical variation can removed including surrogate variables estimated WSVA. , can set includeWsva TRUE, can specify number surrogate variables use n.sv parameter. , surrogate variables can tuned variable genes setting weight..sd TRUE. Please note recommend remove batch effects directly expression measurements prior correlation analysis. function used remove batch effects differential expression analysis, purpose, better include batch variables linear model. way, underestimate residual degrees freedom, calculated standard errors, t-statistics p-values overoptimistic.","code":""},{"path":"/reference/batchCorrection.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Correct for batch effects in miRNA and gene expression measurements — batchCorrection","text":"estimate surrogate variables remove batch effects expression data, MIRit uses limma::wsva() limma::removeBatchEffect() functions, respectively.","code":""},{"path":"/reference/batchCorrection.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Correct for batch effects in miRNA and gene expression measurements — batchCorrection","text":"Ritchie , Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015). “limma powers differential expression analyses RNA-sequencing microarray studies.” Nucleic Acids Research, 43(7), e47. doi:10.1093/nar/gkv007.","code":""},{"path":"/reference/batchCorrection.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Correct for batch effects in miRNA and gene expression measurements — batchCorrection","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/batchCorrection.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Correct for batch effects in miRNA and gene expression measurements — batchCorrection","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # correct batch effects due to the patient from miRNA expression matrix obj <- batchCorrection(obj, \"microRNA\", batch = \"patient\")"},{"path":"/reference/deAccessors.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract differentially expressed miRNAs and genes from a\nMirnaExperiment object — deAccessors","title":"Extract differentially expressed miRNAs and genes from a\nMirnaExperiment object — deAccessors","text":"mirnaDE() geneDE() two accessor functions mirnaDE geneDE slots MirnaExperiment class, respectively. Thus, can used explore results miRNA gene differential expression analysis stored MirnaExperiment object.","code":""},{"path":"/reference/deAccessors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract differentially expressed miRNAs and genes from a\nMirnaExperiment object — deAccessors","text":"","code":"mirnaDE(object, onlySignificant = TRUE, param = FALSE, returnObject = FALSE)  geneDE(object, onlySignificant = TRUE, param = FALSE, returnObject = FALSE)"},{"path":"/reference/deAccessors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract differentially expressed miRNAs and genes from a\nMirnaExperiment object — deAccessors","text":"object MirnaExperiment object containing miRNA gene data onlySignificant Logical, TRUE differential expression results returned just statistically significant miRNAs/genes, FALSE full table miRNA/gene differential expression provided. Default TRUE report significant miRNAs/genes param Logical, whether return complete list object parameters used, just results stored data. Default FALSE returnObject Logical, TRUE function return limma/edgeR/DESeq2 object used differential expression analysis","code":""},{"path":"/reference/deAccessors.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract differentially expressed miRNAs and genes from a\nMirnaExperiment object — deAccessors","text":"data.frame miRNA/gene differential expression, list object parameters used param = TRUE.","code":""},{"path":"/reference/deAccessors.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Extract differentially expressed miRNAs and genes from a\nMirnaExperiment object — deAccessors","text":"mirnaDE(): Extract miRNA differential expression results geneDE(): Extract gene differential expression results","code":""},{"path":"/reference/deAccessors.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract differentially expressed miRNAs and genes from a\nMirnaExperiment object — deAccessors","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/deAccessors.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract differentially expressed miRNAs and genes from a\nMirnaExperiment object — deAccessors","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # access miRNA differential expression of a MirnaExperiment object sig <- mirnaDE(obj) all <- mirnaDE(obj, onlySignificant = FALSE)  # access gene differential expression of a MirnaExperiment object sig <- geneDE(obj) all <- geneDE(obj, onlySignificant = FALSE)"},{"path":"/reference/deAnalysis.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform differential expression analysis — deAnalysis","title":"Perform differential expression analysis — deAnalysis","text":"performMirnaDE() performGeneDE() two functions provided MIRit conduct miRNA gene differential expression analysis, respectively. particular, functions allow user compute differential expression different methods, namely edgeR, DESeq2, limma-voom limma. Data deriving NGS experiments microarray technology suitable functions. precise indications use functions, please refer details section.","code":""},{"path":"/reference/deAnalysis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform differential expression analysis — deAnalysis","text":"","code":"performMirnaDE(   mirnaObj,   group,   contrast,   design,   method = \"edgeR\",   logFC = 1,   pCutoff = 0.05,   pAdjustment = \"fdr\",   filterByExpr.args = list(),   calcNormFactors.args = list(),   estimateDisp.args = list(robust = TRUE),   glmQLFit.args = list(),   glmQLFTest.args = list(),   DESeq.args = list(),   useVoomWithQualityWeights = TRUE,   voom.args = list(),   lmFit.args = list(),   eBayes.args = list(),   useArrayWeights = TRUE,   useWsva = FALSE,   wsva.args = list(),   arrayWeights.args = list(),   useDuplicateCorrelation = FALSE,   correlationBlockVariable = NULL,   duplicateCorrelation.args = list() )  performGeneDE(   mirnaObj,   group,   contrast,   design,   method = \"edgeR\",   logFC = 1,   pCutoff = 0.05,   pAdjustment = \"fdr\",   filterByExpr.args = list(),   calcNormFactors.args = list(),   estimateDisp.args = list(robust = TRUE),   glmQLFit.args = list(),   glmQLFTest.args = list(),   DESeq.args = list(),   useVoomWithQualityWeights = TRUE,   voom.args = list(),   lmFit.args = list(),   eBayes.args = list(),   useArrayWeights = TRUE,   useWsva = FALSE,   wsva.args = list(),   arrayWeights.args = list(),   useDuplicateCorrelation = FALSE,   correlationBlockVariable = NULL,   duplicateCorrelation.args = list() )"},{"path":"/reference/deAnalysis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform differential expression analysis — deAnalysis","text":"mirnaObj MirnaExperiment object containing miRNA gene data group variable interest differential expression analysis. must column name variable present metadata (colData) MirnaExperiment object. See details section additional information contrast character object specifies groups compared differential expression analysis, separated dash (e.g. 'disease-healthy'). Note reference group must last one, additional information see details section design R formula indicates model fit. must include variable interest (group) together eventual covariates (e.g. '~ 0 + disease + sex'). Please note group variable must first one. See details section additional information method statistical package used compute differential expression. NGS experiments, must one edgeR (default), DESeq2, voom (limma-voom). Instead, microarray data, limma can used logFC minimum log2 fold change required consider gene differentially expressed. Default 1, retain two-fold differences pCutoff adjusted p-value cutoff use statistical significance. default value 0.05 pAdjustment p-value correction method multiple testing. must one : fdr (default), BH, none, holm, hochberg, hommel, bonferroni, filterByExpr.args list object containing additional arguments passed edgeR::filterByExpr() function. used method set edgeR voom calcNormFactors.args list object containing additional arguments passed edgeR::calcNormFactors() function. used method set edgeR voom estimateDisp.args list object containing additional arguments passed edgeR::estimateDisp() function. used method set edgeR. Default list(robust = TRUE) use robust parameter glmQLFit.args list object containing additional arguments passed edgeR::glmQLFit() function. used method set edgeR glmQLFTest.args list object containing additional arguments passed edgeR::glmQLFTest() function. used method set edgeR DESeq.args list object containing additional arguments passed DESeq2::DESeq() function. used method set DESeq useVoomWithQualityWeights Logical, whether use limma::voomWithQualityWeights() function just limma::voom() function. used method set voom. Default TRUE voom.args list object containing additional arguments passed limma::voom() function limma::voomWithQualityWeights() function. used method set voom lmFit.args list object containing additional arguments passed limma::lmFit() function. used method set voom limma eBayes.args list object containing additional arguments passed limma::eBayes() function. used method set voom limma useArrayWeights Logical, whether use limma::arrayWeights() function . used method set limma. Default TRUE useWsva Logical, whether use limma::wsva() function . used method set limma. Default FALSE wsva.args list object containing additional arguments passed limma::wsva() function. used method set limma arrayWeights.args list object containing additional arguments passed limma::arrayWeights() function. used method set limma useDuplicateCorrelation Logical, whether use limma::duplicateCorrelation() function . used method set limma. Default FALSE correlationBlockVariable blocking variable use limma::duplicateCorrelation(). Default NULL duplicateCorrelation.args list object containing additional arguments passed limma::duplicateCorrelation() function. used method set limma","code":""},{"path":"/reference/deAnalysis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform differential expression analysis — deAnalysis","text":"MirnaExperiment object containing differential expression results. access results, user may run mirnaDE() geneDE() functions miRNAs genes, respectively.","code":""},{"path":"/reference/deAnalysis.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Perform differential expression analysis — deAnalysis","text":"performing differential expression NGS experiments, count matrices detected method parameter must one edgeR, DESeq2, voom. hand, dealing microarray studies, limma can used. calculate differential expression, MIRit must informed variable interest desired contrast. particular, group parameter must name variable present metadata (colData) MirnaExperiment object, specifies variable used compute differential expression analysis, groups indicated contrast. Specifically, contrast must character vector defines levels compare separated dash. example, variable named 'condition', two levels, namely 'disease' 'healthy', can identify differentially expressed genes 'disease' samples compared 'healthy' subjects specifying: group = 'condition' contrast = 'disease-healthy'. Furthermore, user needs specify model fit expression values. , user state model formula design parameter. Please note correct inner working functions, group variable interest must first variable model formula. Moreover, user can include design sources variation specifying covariates taken account. instance, want compare 'disease' subjects 'healthy' individuals, without influence sex differences, may specify design = ~ condition + sex, 'sex' also variable present metadata (colData) mirnaObj. Notably, methods available, user can supply additional arguments functions implemented edgeR, DESeq2 limma. Therefore, user finer control differential expression analysis performed. regard, microarray studies, user may opt include weighted surrogate variable analysis (WSVA) correct unknown sources variation (useWsva = TRUE). Moreover, microarray data, arrayWeights() function limma can used assess differential expression respect array qualities. Also, duplicateCorrelation() function limma may included pipeline order block effect correlated samples. , user must set useDuplicateCorrelation = TRUE, must specify blocking variable correlationBlockVariable parameter. Additionally, using limma-voom, user may estimate voom transformation without quality weights (specifying useVoomWithQualityWeights = TRUE).","code":""},{"path":"/reference/deAnalysis.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Perform differential expression analysis — deAnalysis","text":"performMirnaDE(): Perform differential expression analysis miRNAs performGeneDE(): Perform differential expression analysis genes","code":""},{"path":"/reference/deAnalysis.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Perform differential expression analysis — deAnalysis","text":"Ritchie , Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015). “limma powers differential expression analyses RNA-sequencing microarray studies.” Nucleic Acids Research, 43(7), e47. doi:10.1093/nar/gkv007. Law, CW, Chen, Y, Shi, W, Smyth, GK (2014). \"Voom: precision weights unlock linear model analysis tools RNA-seq read counts\". Genome Biology 15, R29 Robinson MD, McCarthy DJ, Smyth GK (2010). “edgeR: Bioconductor package differential expression analysis digital gene expression data.” Bioinformatics, 26(1), 139-140. doi:10.1093/bioinformatics/btp616. Love MI, Huber W, Anders S (2014). “Moderated estimation fold change dispersion RNA-seq data DESeq2.” Genome Biology, 15, 550. doi:10.1186/s13059-014-0550-8.","code":""},{"path":"/reference/deAnalysis.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Perform differential expression analysis — deAnalysis","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/deAnalysis.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform differential expression analysis — deAnalysis","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # perform miRNA DE with edgeR obj <- performMirnaDE(obj,     group = \"disease\", contrast = \"PTC-NTH\",     design = ~ 0 + disease + patient, method = \"edgeR\" ) #> Performing differential expression analysis with edgeR... #> Differential expression analysis reported 40 significant miRNAs with p < 0.05 (correction: fdr). You can use the 'mirnaDE()' function to access results.  # perform miRNA DE with DESeq2 obj <- performMirnaDE(obj,     group = \"disease\", contrast = \"PTC-NTH\",     design = ~ 0 + disease + patient, method = \"DESeq2\" ) #> Performing differential expression analysis with DESeq2... #> Warning: some variables in design formula are characters, converting to factors #> estimating size factors #> estimating dispersions #> gene-wise dispersion estimates #> mean-dispersion relationship #> final dispersion estimates #> fitting model and testing #> Differential expression analysis reported 58 significant miRNAs with p < 0.05 (correction: fdr). You can use the 'mirnaDE()' function to access results.  # perform gene DE with limma-voom obj <- performGeneDE(obj,     group = \"disease\", contrast = \"PTC-NTH\",     design = ~ 0 + disease + patient, method = \"voom\" ) #> Performing differential expression analysis with voom... #> Differential expression analysis reported 260 significant genes with p < 0.05 (correction: fdr). You can use the 'geneDE()' function to access results."},{"path":"/reference/enrichGenes.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform functional enrichment analysis of genes — enrichGenes","title":"Perform functional enrichment analysis of genes — enrichGenes","text":"function allows investigate biological functions pathways result dysregulated across biological conditions. particular, different enrichment approaches can used, including -representation analysis (ORA), gene-set enrichment analysis (GSEA), Correlation Adjusted MEan RAnk gene set test (CAMERA). Moreover, analyses, enrichment can carried using different databases, namely Gene Ontology (GO), Kyoto Encyclopedia Genes Genomes (KEGG), MsigDB, WikiPathways, Reactome, Enrichr, Disease Ontology (), Network Cancer Genes (NCG), DisGeNET, COVID19. exhaustive information use function, please refer details section.","code":""},{"path":"/reference/enrichGenes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform functional enrichment analysis of genes — enrichGenes","text":"","code":"enrichGenes(   mirnaObj,   method = \"GSEA\",   database = \"GO\",   category = NULL,   organism = \"Homo sapiens\",   pCutoff = 0.05,   pAdjustment = \"fdr\",   minSize = 10L,   maxSize = 500L,   rankMetric = \"signed.pval\",   eps = 1e-50 )"},{"path":"/reference/enrichGenes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform functional enrichment analysis of genes — enrichGenes","text":"mirnaObj MirnaExperiment object containing miRNA gene data method functional enrichment analysis perform. must one ORA, GSEA (default), CAMERA. additional information, see details section database name database used enrichment analysis. must one : GO, KEGG, MsigDB, WikiPathways, Reactome, Enrichr, , NCG, DisGeNET, COVID19. Default GO category desired subcategory gene sets present database. Please, see details section check available categories database. Default NULL use default categories organism name organism consideration. different databases different supported organisms. see list supported organisms given database, use supportedOrganisms() function. Default Homo sapiens pCutoff adjusted p-value cutoff use statistical significance. default value 0.05 pAdjustment p-value correction method multiple testing. must one : fdr (default), BH, none, holm, hochberg, hommel, bonferroni, minSize minimum size gene set. gene sets containing less number genes considered. Default 10 maxSize maximum size gene set. gene sets containing number genes considered. Default 500 rankMetric ranking statistic used order genes performing GSEA. must one signed.pval (default), logFC, log.pval. additional information, refer details section eps lower boundary p-value calculation (default 1e-50). compute exact p-values, parameter can set 0, even though analysis slower","code":""},{"path":"/reference/enrichGenes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform functional enrichment analysis of genes — enrichGenes","text":"method GSEA CAMERA, function produces object class FunctionalEnrichment containing enrichment results. Instead, ORA used, function returns list object two elements, namely 'upregulated' 'downregulated', containing FunctionalEnrichment object storing enrichment results upregulated downregulated genes, respectively. access results FunctionalEnrichment objects, user can use enrichmentResults() function. Additionally, MIRit provides several functions graphically represent enrichment analyses, including enrichmentBarplot(), enrichmentDotplot(), gseaPlot(), gseaRidgeplot().","code":""},{"path":[]},{"path":"/reference/enrichGenes.html","id":"enrichment-method","dir":"Reference","previous_headings":"","what":"Enrichment method","title":"Perform functional enrichment analysis of genes — enrichGenes","text":"method used functional enrichment analysis drastically influence biological results, thus, must carefully chosen. ORA (Boyle et al., 2004) takes differentially expressed genes (separately considering upregulated downregulated features) uses hypergeometric test infer biological processes regulated genes expected chance. downside approach consider genes passed pre-defined threshold, thus losing slight changes gene expression may important biological consequences. address limit, GSEA introduced (Subramanian, 2005). analysis starts ranking genes according specific criterion, uses running statistic able identify even slight coordinated expression changes genes belonging specific pathway. Therefore, GSEA default method used MIRit perform functional enrichment analysis genes. Moreover, addition ORA GSEA, function allows perform enrichment analysis CAMERA (Wu Smyth, 2012), another competitive test used functional enrichment genes. main advantage method adjusts gene set test statistic according inter-gene correlations. particularly interesting since demonstrated inter-gene correlations may affect reliability functional enrichment analyses.","code":""},{"path":"/reference/enrichGenes.html","id":"databases-and-categories","dir":"Reference","previous_headings":"","what":"Databases and categories","title":"Perform functional enrichment analysis of genes — enrichGenes","text":"Regarding gene sets, multiple databases can used investigate consequences gene expression alterations. However, different databases also includes several subcategories different annotations. specifically query desired categories, category parameter used. reference, listed available categories different databases supported: Gene Ontology (GO): bp, GO - Biological Processes; mf, GO - Molecular Function; cc, GO - Cellular Component; Kyoto Encyclopedia Genes Genomes (KEGG): pathway, KEGG biological pathways; module, KEGG reaction modules; enzyme, KEGG enzyme nomenclature; disease, KEGG diseases (Homo sapiens supported); drug, KEGG drug targets (Homo sapiens supported); network, KEGG disease/drug perturbation netowrks (Homo sapiens supported); MsigDB: H, MsigDB hallmark genes specific biological states/processes; C1, gene sets human chromosome cytogenetic bands; C2-CGP, expression signatures genetic chemical perturbations; C2-CP-BIOCARTA, canonical pathways gene sets derived BioCarta pathway database; C2-CP-KEGG, canonical pathways gene sets derived KEGG pathway database; C2-CP-PID, canonical pathways gene sets derived PID pathway database; C2-CP-REACTOME, canonical pathways gene sets derived Reactome pathway database; C2-CP-WIKIPATHWAYS, canonical pathways gene sets derived WikiPathways database; C3-MIR-MIRDB, gene sets containing high-confidence gene-level predictions human miRNA targets catalogued miRDB v6.0 algorithm; C3-MIR-MIR_Legacy, older gene sets contain genes sharing putative target sites human mature miRNA 3'-UTRs; C3-TFT-GTRD, genes share GTRD predicted transcription factor binding sites region -1000,+100 bp around TSS indicated transcription factor; C3-TFT-TFT_Legacy, older gene sets share upstream cis-regulatory motifs can function potential transcription factor binding sites; C4-CGN, gene sets defined expression neighborhoods centered 380 cancer-associated genes; C4-CM, cancer modules defined Segal et al. 2004; C5-GO-BP, GO - biological process ontology; C5-GO-CC, GO - cellular component ontology; C5-GO-MF, GO - molecular function ontology; C5-HPO, Human Phenotype ontology (HPO); C6, gene sets represent signatures cellular pathways often dis-regulated cancer; C7-IMMUNESIGDB, manually curated gene sets representing chemical genetic perturbations immune system; C7-VAX, gene sets deriving Human Immunology Project Consortium (HIPC) describing human transcriptomic immune responses vaccinations; C8, gene sets contain curated cluster markers cell types; WikiPathways; Reactome; Enrichr: avaliable gene sets can listed geneset::enrichr_metadata Disease Ontology (); Network Cancer Genes (NCG): v6, sixth version; v7, seventh version; DisGeNET; COVID-19.","code":""},{"path":"/reference/enrichGenes.html","id":"supported-organisms","dir":"Reference","previous_headings":"","what":"Supported organisms","title":"Perform functional enrichment analysis of genes — enrichGenes","text":"database, different organisms supported. check supported organisms given database, MIRit provides supportedOrganisms() function.","code":""},{"path":"/reference/enrichGenes.html","id":"gsea-ranking-statistic","dir":"Reference","previous_headings":"","what":"GSEA ranking statistic","title":"Perform functional enrichment analysis of genes — enrichGenes","text":"ranking statistic used order genes conducting GSEA able influence biological interpretation functional enrichment results. Several metrics used scientific literature. MIRit implements possibility using signed.pval, logFC, log.pval. particular, simplest option rank genes according logFC value. However, procedure biased higher variance lowly abundant genes. Therefore, recommend use signed.pval metric, consists p-value gene multiplied sign logFC, .e. sign(logFC) * p-value. Alternatively, log,pval metric, consist product logFC p-value, .e. logFC * p-value can also used.","code":""},{"path":"/reference/enrichGenes.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Perform functional enrichment analysis of genes — enrichGenes","text":"download gene sets mentioned databases, MIRit uses geneset R package. Moreover, perform ORA GSEA, MIRit implements fgsea algorithm, whereas CAMERA, limma package used.","code":""},{"path":"/reference/enrichGenes.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Perform functional enrichment analysis of genes — enrichGenes","text":"Liu, Y., Li, G. Empowering biologists decode omics data: Genekitr R package web server. BMC Bioinformatics 24, 214 (2023). https://doi.org/10.1186/s12859-023-05342-9. Korotkevich G, Sukhov V, Sergushichev (2019). “Fast gene set enrichment analysis.” bioRxiv. doi:10.1101/060012, http://biorxiv.org/content/early/2016/06/20/060012. Ritchie , Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015). “limma powers differential expression analyses RNA-sequencing microarray studies.” Nucleic Acids Research, 43(7), e47. doi:10.1093/nar/gkv007. Wu D, Smyth GK. Camera: competitive gene set test accounting inter-gene correlation. Nucleic Acids Res. 2012 Sep 1;40(17):e133. doi: 10.1093/nar/gks461. Epub 2012 May 25. PMID: 22638577; PMCID: PMC3458527.","code":""},{"path":"/reference/enrichGenes.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Perform functional enrichment analysis of genes — enrichGenes","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/enrichGenes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform functional enrichment analysis of genes — enrichGenes","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # perform GSEA with KEGG de_enr <- enrichGenes(obj, method = \"GSEA\", database = \"KEGG\") #> Since not specified, 'category' for KEGG database is set to pathway (default). #> Preparing the appropriate gene set... #> Some ID occurs one-to-many match, like \"79154, 7920, 79143\"... #> 99.96% genes are mapped to symbol #> Ranking genes based on signed.pval... #> Performing gene-set enrichment analysis (GSEA)... #> GSEA reported 2 significantly enriched terms.  # extract results de_df <- enrichmentResults(de_enr)  # create a dotplot of enriched terms enrichmentDotplot(de_enr)"},{"path":"/reference/enrichTargets.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform an enrichment analysis of integrated microRNA targets — enrichTargets","title":"Perform an enrichment analysis of integrated microRNA targets — enrichTargets","text":"function allows perform -representation analysis (ORA) integrated miRNA targets order explore biological effects targets statistically associated/correlated DE-miRNAs. enrichment analysis can performed using different databases, namely Gene Ontology (GO), Kyoto Encyclopedia Genes Genomes (KEGG), MsigDB, WikiPathways, Reactome, Enrichr, Disease Ontology (), Network Cancer Genes (NCG), DisGeNET, COVID19.","code":""},{"path":"/reference/enrichTargets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform an enrichment analysis of integrated microRNA targets — enrichTargets","text":"","code":"enrichTargets(   mirnaObj,   database = \"GO\",   category = NULL,   organism = \"Homo sapiens\",   pCutoff = 0.05,   pAdjustment = \"fdr\",   minSize = 10L,   maxSize = 500L )"},{"path":"/reference/enrichTargets.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform an enrichment analysis of integrated microRNA targets — enrichTargets","text":"mirnaObj MirnaExperiment object containing miRNA gene data database name database used enrichment analysis. must one : GO, KEGG, MsigDB, WikiPathways, Reactome, Enrichr, , NCG, DisGeNET, COVID19. Default GO category desired subcategory gene sets present database. Please, see details section check available categories database. Default NULL use default categories organism name organism consideration. different databases different supported organisms. see list supported organisms given database, use supportedOrganisms() function. Default Homo sapiens pCutoff adjusted p-value cutoff use statistical significance. default value 0.05 pAdjustment p-value correction method multiple testing. must one : fdr (default), BH, none, holm, hochberg, hommel, bonferroni, minSize minimum size gene set. gene sets containing less number genes considered. Default 10 maxSize maximum size gene set. gene sets containing number genes considered. Default 500","code":""},{"path":"/reference/enrichTargets.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform an enrichment analysis of integrated microRNA targets — enrichTargets","text":"function produces list object two elements, namely 'upregulated' 'downregulated', containing FunctionalEnrichment object storing enrichment results upregulated downregulated target genes, respectively. access results FunctionalEnrichment objects, user can use enrichmentResults() function. Additionally, MIRit provides several functions graphically represent enrichment analyses, including enrichmentBarplot(), enrichmentDotplot().","code":""},{"path":"/reference/enrichTargets.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Perform an enrichment analysis of integrated microRNA targets — enrichTargets","text":"database, different organisms supported. check supported organisms given database, MIRit provides supportedOrganisms() function. Moreover, since different database support multiple subcategories, category parameter can set specify desired resource. specific information regarding available categories different databases, check details section enrichGenes() documentation.","code":""},{"path":"/reference/enrichTargets.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Perform an enrichment analysis of integrated microRNA targets — enrichTargets","text":"download gene sets mentioned databases, MIRit uses geneset R package. Moreover, perform ORA, MIRit implements fgsea package Bioconductor.","code":""},{"path":"/reference/enrichTargets.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Perform an enrichment analysis of integrated microRNA targets — enrichTargets","text":"Liu, Y., Li, G. Empowering biologists decode omics data: Genekitr R package web server. BMC Bioinformatics 24, 214 (2023). https://doi.org/10.1186/s12859-023-05342-9. Korotkevich G, Sukhov V, Sergushichev (2019). “Fast gene set enrichment analysis.” bioRxiv. doi:10.1101/060012, http://biorxiv.org/content/early/2016/06/20/060012.","code":""},{"path":"/reference/enrichTargets.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Perform an enrichment analysis of integrated microRNA targets — enrichTargets","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/enrichTargets.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform an enrichment analysis of integrated microRNA targets — enrichTargets","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # perform enrichment analysis of integrated targets with DO targets_enrichment <- enrichTargets(obj, database = \"DO\") #> Preparing the appropriate gene set... #> Some ID occurs one-to-many match, like \"26476, 127068, 101060321\"... #> 99.06% genes are mapped to symbol #> Performing the enrichment of upregulated genes... #> Performing the enrichment of downregulated genes... #> The enrichment of genes reported 113 significantly enriched terms for downregulated genes and 0 for upregulated genes.  # extract enrichment results of downregulated targets enr_down <- targets_enrichment[[\"downregulated\"]]  # extract enrichment results as a data.frame enr_df <- enrichmentResults(enr_down)  # create a dotplot of enriched terms enrichmentDotplot(enr_down)"},{"path":"/reference/enrichedFeatures.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the names of the pre-ranked features in a GSEA experiment — enrichedFeatures","title":"Extract the names of the pre-ranked features in a GSEA experiment — enrichedFeatures","text":"function accesses features slot FunctionalEnrichment object returns character vector names features considered GSEA order ranking metric.","code":""},{"path":"/reference/enrichedFeatures.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the names of the pre-ranked features in a GSEA experiment — enrichedFeatures","text":"","code":"enrichedFeatures(object)"},{"path":"/reference/enrichedFeatures.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the names of the pre-ranked features in a GSEA experiment — enrichedFeatures","text":"object object class FunctionalEnrichment containing enrichment results","code":""},{"path":"/reference/enrichedFeatures.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the names of the pre-ranked features in a GSEA experiment — enrichedFeatures","text":"character vector names genes ordered based ranking metric.","code":""},{"path":"/reference/enrichedFeatures.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract the names of the pre-ranked features in a GSEA experiment — enrichedFeatures","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/enrichedFeatures.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the names of the pre-ranked features in a GSEA experiment — enrichedFeatures","text":"","code":"# load example FunctionalEnrichment object obj <- loadExamples(\"FunctionalEnrichment\")  # extract the ranking metric rmet <- enrichmentMetric(obj)  ## extract the corresponding names rnames <- enrichedFeatures(obj)"},{"path":"/reference/enrichmentBarplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a barplot for functional enrichment analysis — enrichmentBarplot","title":"Create a barplot for functional enrichment analysis — enrichmentBarplot","text":"function produces barplot show results functional enrichment analyses carried -representation analysis (ORA), gene set enrichment analysis (GSEA), competitive gene set test accounting inter-gene correlation (CAMERA). particular, function can take input enrichment results generated enrichGenes() function.","code":""},{"path":"/reference/enrichmentBarplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a barplot for functional enrichment analysis — enrichmentBarplot","text":"","code":"enrichmentBarplot(   enrichment,   showTerms = 10,   showTermsParam = \"ratio\",   splitDir = TRUE,   title = NULL )"},{"path":"/reference/enrichmentBarplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a barplot for functional enrichment analysis — enrichmentBarplot","text":"enrichment object class FunctionalEnrichment containing enrichment results showTerms number terms shown, based order determined parameter showTermsParam; , alternatively, character vector indicating terms plot. Default 10 showTermsParam order top terms selected specified showTerms parameter. must one ratio (default), padj, pval overlap splitDir Logical, TRUE resulting plot divided two columns basis enrichment direction (). Default TRUE. applies enrichment method GSEA CAMERA title title plot. Default NULL include plot title","code":""},{"path":"/reference/enrichmentBarplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a barplot for functional enrichment analysis — enrichmentBarplot","text":"ggplot graph barplot enrichment results.","code":""},{"path":"/reference/enrichmentBarplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a barplot for functional enrichment analysis — enrichmentBarplot","text":"producing barplot function, significant pathways ordered x-axis basis ratio number overlapping genes set, total number genes set. Moreover, color scale dots relative adjusted p-values category.","code":""},{"path":"/reference/enrichmentBarplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create a barplot for functional enrichment analysis — enrichmentBarplot","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/enrichmentBarplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a barplot for functional enrichment analysis — enrichmentBarplot","text":"","code":"# load example FunctionalEnrichment object obj <- loadExamples(\"FunctionalEnrichment\")  # extract results res <- enrichmentResults(obj)  # plot results enrichmentBarplot(obj)"},{"path":"/reference/enrichmentDatabase.html","id":null,"dir":"Reference","previous_headings":"","what":"Access the database used for functional enrichment analyses — enrichmentDatabase","title":"Access the database used for functional enrichment analyses — enrichmentDatabase","text":"function accesses database slot FunctionalEnrichment object returns name database used enrichGenes() function perform enrichment analysis.","code":""},{"path":"/reference/enrichmentDatabase.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access the database used for functional enrichment analyses — enrichmentDatabase","text":"","code":"enrichmentDatabase(object)"},{"path":"/reference/enrichmentDatabase.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access the database used for functional enrichment analyses — enrichmentDatabase","text":"object object class FunctionalEnrichment containing enrichment results","code":""},{"path":"/reference/enrichmentDatabase.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access the database used for functional enrichment analyses — enrichmentDatabase","text":"character containing name database, KEGG.","code":""},{"path":"/reference/enrichmentDatabase.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Access the database used for functional enrichment analyses — enrichmentDatabase","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/enrichmentDatabase.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access the database used for functional enrichment analyses — enrichmentDatabase","text":"","code":"# load example FunctionalEnrichment object obj <- loadExamples(\"FunctionalEnrichment\")  # see the database enrichmentDatabase(obj) #> [1] \"KEGG (category: pathway)\""},{"path":"/reference/enrichmentDotplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a dotplot for functional enrichment analysis — enrichmentDotplot","title":"Create a dotplot for functional enrichment analysis — enrichmentDotplot","text":"function produces dotplot show results functional enrichment analyses carried -representation analysis (ORA), gene set enrichment analysis (GSEA), competitive gene set test accounting inter-gene correlation (CAMERA). particular, function can take input enrichment results generated enrichGenes() function.","code":""},{"path":"/reference/enrichmentDotplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a dotplot for functional enrichment analysis — enrichmentDotplot","text":"","code":"enrichmentDotplot(   enrichment,   showTerms = 10,   showTermsParam = \"ratio\",   splitDir = TRUE,   title = NULL )"},{"path":"/reference/enrichmentDotplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a dotplot for functional enrichment analysis — enrichmentDotplot","text":"enrichment object class FunctionalEnrichment containing enrichment results showTerms number terms shown, based order determined parameter showTermsParam; , alternatively, character vector indicating terms plot. Default 10 showTermsParam order top terms selected specified showTerms parameter. must one ratio (default), padj, pval overlap splitDir Logical, TRUE resulting plot divided two columns basis enrichment direction (). Default TRUE. applies enrichment method GSEA CAMERA title title plot. Default NULL include plot title","code":""},{"path":"/reference/enrichmentDotplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a dotplot for functional enrichment analysis — enrichmentDotplot","text":"ggplot graph dotplot enrichment results.","code":""},{"path":"/reference/enrichmentDotplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a dotplot for functional enrichment analysis — enrichmentDotplot","text":"producing dotplot function, significant pathways ordered x-axis basis ratio number overlapping genes set, total number genes set. Moreover, size dot proportional number overlapping features. Finally, color scale dots relative adjusted p-values category.","code":""},{"path":"/reference/enrichmentDotplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create a dotplot for functional enrichment analysis — enrichmentDotplot","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/enrichmentDotplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a dotplot for functional enrichment analysis — enrichmentDotplot","text":"","code":"# load example FunctionalEnrichment object obj <- loadExamples(\"FunctionalEnrichment\")  # extract results res <- enrichmentResults(obj)  # plot results enrichmentDotplot(obj)"},{"path":"/reference/enrichmentMethod.html","id":null,"dir":"Reference","previous_headings":"","what":"Access the method used for functional enrichment analyses — enrichmentMethod","title":"Access the method used for functional enrichment analyses — enrichmentMethod","text":"function accesses method slot FunctionalEnrichment object returns name enrichment strategy used enrichGenes() function perform enrichment analysis.","code":""},{"path":"/reference/enrichmentMethod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access the method used for functional enrichment analyses — enrichmentMethod","text":"","code":"enrichmentMethod(object)"},{"path":"/reference/enrichmentMethod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access the method used for functional enrichment analyses — enrichmentMethod","text":"object object class FunctionalEnrichment containing enrichment results","code":""},{"path":"/reference/enrichmentMethod.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access the method used for functional enrichment analyses — enrichmentMethod","text":"character containing enrichment method, GSEA.","code":""},{"path":"/reference/enrichmentMethod.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Access the method used for functional enrichment analyses — enrichmentMethod","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/enrichmentMethod.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access the method used for functional enrichment analyses — enrichmentMethod","text":"","code":"# load example FunctionalEnrichment object obj <- loadExamples(\"FunctionalEnrichment\")  # see the method enrichmentMethod(obj) #> [1] \"Gene-Set Enrichment Analysis (GSEA)\""},{"path":"/reference/enrichmentMetric.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the GSEA ranking metric used for functional enrichment analyses — enrichmentMetric","title":"Extract the GSEA ranking metric used for functional enrichment analyses — enrichmentMetric","text":"function accesses statistic slot FunctionalEnrichment object returns numeric vector metric used rank genes GSEA.","code":""},{"path":"/reference/enrichmentMetric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the GSEA ranking metric used for functional enrichment analyses — enrichmentMetric","text":"","code":"enrichmentMetric(object)"},{"path":"/reference/enrichmentMetric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the GSEA ranking metric used for functional enrichment analyses — enrichmentMetric","text":"object object class FunctionalEnrichment containing enrichment results","code":""},{"path":"/reference/enrichmentMetric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the GSEA ranking metric used for functional enrichment analyses — enrichmentMetric","text":"numeric vector containing ranking metric.","code":""},{"path":"/reference/enrichmentMetric.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract the GSEA ranking metric used for functional enrichment analyses — enrichmentMetric","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/enrichmentMetric.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the GSEA ranking metric used for functional enrichment analyses — enrichmentMetric","text":"","code":"# load example FunctionalEnrichment object obj <- loadExamples(\"FunctionalEnrichment\")  # extract the ranking metric rmet <- enrichmentMetric(obj)"},{"path":"/reference/enrichmentResults.html","id":null,"dir":"Reference","previous_headings":"","what":"Access the results of functional enrichment analyses — enrichmentResults","title":"Access the results of functional enrichment analyses — enrichmentResults","text":"function accesses data slot FunctionalEnrichment object returns data.frame enrichment results.","code":""},{"path":"/reference/enrichmentResults.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access the results of functional enrichment analyses — enrichmentResults","text":"","code":"enrichmentResults(object)"},{"path":"/reference/enrichmentResults.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access the results of functional enrichment analyses — enrichmentResults","text":"object object class FunctionalEnrichment containing enrichment results","code":""},{"path":"/reference/enrichmentResults.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access the results of functional enrichment analyses — enrichmentResults","text":"data.frame object containing results functional enrichment analyses, returned enrichGenes() function.","code":""},{"path":"/reference/enrichmentResults.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Access the results of functional enrichment analyses — enrichmentResults","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/enrichmentResults.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access the results of functional enrichment analyses — enrichmentResults","text":"","code":"# load example FunctionalEnrichment object obj <- loadExamples(\"FunctionalEnrichment\")  # extract results de_df <- enrichmentResults(obj)"},{"path":"/reference/findMirnaSNPs.html","id":null,"dir":"Reference","previous_headings":"","what":"Find disease-associated SNPs occurring at DE-miRNA loci — findMirnaSNPs","title":"Find disease-associated SNPs occurring at DE-miRNA loci — findMirnaSNPs","text":"function allows identify disease-associated genomic variants affecting differentially expressed miRNA genes host genes. , function uses gwasrapidd retrieve SNPs-disease associations, retains SNPs affect DE-miRNA genes relative host genes (intronic miRNAs).","code":""},{"path":"/reference/findMirnaSNPs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find disease-associated SNPs occurring at DE-miRNA loci — findMirnaSNPs","text":"","code":"findMirnaSNPs(mirnaObj, diseaseEFO)"},{"path":"/reference/findMirnaSNPs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find disease-associated SNPs occurring at DE-miRNA loci — findMirnaSNPs","text":"mirnaObj MirnaExperiment object containing miRNA gene data diseaseEFO EFO identifier disease interest. can identified searchDisease() function","code":""},{"path":"/reference/findMirnaSNPs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find disease-associated SNPs occurring at DE-miRNA loci — findMirnaSNPs","text":"data.frame containing details disease-SNPs associated differentially expressed miRNAs: variant contains SNP identifiers; gene defines gene affected disease-SNP (may miRNA gene host gene intronic miRNA); miRNA.gene specifies DE-miRNA gene present; miRNA.precursor specifies name miRNA precursor affected disease-SNPs; chr indicates chromosome SNPs; position shows SNP position; allele displays possible alleles SNPs; distance specifies distance SNPs miRNAs; is_upstream indicates whether SNP upstream miRNA gene; is_downstream indicates whether SNP downstream miRNA gene; mirnaStrand shows strand; mirnaStartPosition displays start position DE-miRNA gene; mirnaEndPosition displays end position DE-miRNA gene.","code":""},{"path":"/reference/findMirnaSNPs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Find disease-associated SNPs occurring at DE-miRNA loci — findMirnaSNPs","text":"SNPs occurring within miRNAs may important effects biological function transcripts. Indeed, SNP present within miRNA gene might alter expression spectrum miRNA targets. retrieve disease-SNPs, function uses gwasrapidd package, directly queries NHGRI-EBI Catalog published genome-wide association studies. running function, user can use mirVariantPlot() function produce trackplot visualizing genomic location SNPs within miRNA genes.","code":""},{"path":"/reference/findMirnaSNPs.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Find disease-associated SNPs occurring at DE-miRNA loci — findMirnaSNPs","text":"retrieve disease-associated SNPs, function makes use gwasrapidd package.","code":""},{"path":"/reference/findMirnaSNPs.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Find disease-associated SNPs occurring at DE-miRNA loci — findMirnaSNPs","text":"Ramiro Magno, Ana-Teresa Maia, gwasrapidd: R package query, download wrangle GWAS catalog data, Bioinformatics, Volume 36, Issue 2, January 2020, Pages 649–650, https://doi.org/10.1093/bioinformatics/btz605","code":""},{"path":"/reference/findMirnaSNPs.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Find disease-associated SNPs occurring at DE-miRNA loci — findMirnaSNPs","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/findMirnaSNPs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Find disease-associated SNPs occurring at DE-miRNA loci — findMirnaSNPs","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # \\donttest{ # search disease searchDisease(\"Alzheimer disease\") #> Checking for cached EFO traits... #> Reading EFO traits from cache... #> Searching for disease: Alzheimer disease #> [1] \"Alzheimer's disease biomarker measurement\"  #> [2] \"Alzheimer's disease neuropathologic change\" #> [3] \"Alzheimer disease\"                          #> [4] \"late-onset Alzheimers disease\"              #> [5] \"family history of Alzheimer’s disease\"      #> [6] \"age of onset of Alzheimer disease\"          disId <- \"Alzheimer disease\"  # retrieve associated SNPs association <- findMirnaSNPs(obj, disId) #> Querying GWAS Catalog, this may take some time... #> Finding genomic information of differentially expressed miRNAs... #> Error in bmRequest(request = request, httr_config = httr_config, verbose = verbose): Bad Gateway (HTTP 502). # }"},{"path":"/reference/geneCounts.html","id":null,"dir":"Reference","previous_headings":"","what":"Count matrix for gene expression in thyroid cancer — geneCounts","title":"Count matrix for gene expression in thyroid cancer — geneCounts","text":"dataset contains gene expression matrix resulting RNA-Seq analysis thyroid cancer. Specifically, data originate Riesco-Eizaguirre et al (2015), sequenced 8 papillary thyroid carcinomas (PTC) together paired samples normal thyroid tissue. thing done microRNAs order investigate effects target genes. Data included package obtained Gene Expression Omnibus (GEO accession: GSE63511).","code":""},{"path":"/reference/geneCounts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count matrix for gene expression in thyroid cancer — geneCounts","text":"","code":"data(geneCounts)"},{"path":[]},{"path":"/reference/geneCounts.html","id":"genecounts","dir":"Reference","previous_headings":"","what":"geneCounts","title":"Count matrix for gene expression in thyroid cancer — geneCounts","text":"matrix object containing samples columns genes rows.","code":""},{"path":"/reference/geneCounts.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Count matrix for gene expression in thyroid cancer — geneCounts","text":"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63511","code":""},{"path":"/reference/geneCounts.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Count matrix for gene expression in thyroid cancer — geneCounts","text":"Garcilaso Riesco-Eizaguirre et al., “MiR-146b-3p/PAX8/NIS Regulatory Circuit Modulates Differentiation Phenotype Function Thyroid Cells Carcinogenesis,” Cancer Research 75, . 19 (September 30, 2015): 4119–30, https://doi.org/10.1158/0008-5472.CAN-14-3547.","code":""},{"path":"/reference/geneSet.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the gene-sets used for functional enrichment analyses — geneSet","title":"Extract the gene-sets used for functional enrichment analyses — geneSet","text":"function accesses geneSet slot FunctionalEnrichment object returns list collection genes used enrichment.","code":""},{"path":"/reference/geneSet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the gene-sets used for functional enrichment analyses — geneSet","text":"","code":"geneSet(object)"},{"path":"/reference/geneSet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the gene-sets used for functional enrichment analyses — geneSet","text":"object object class FunctionalEnrichment containing enrichment results","code":""},{"path":"/reference/geneSet.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the gene-sets used for functional enrichment analyses — geneSet","text":"list containing gene-sets.","code":""},{"path":"/reference/geneSet.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract the gene-sets used for functional enrichment analyses — geneSet","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/geneSet.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the gene-sets used for functional enrichment analyses — geneSet","text":"","code":"# load example FunctionalEnrichment object obj <- loadExamples(\"FunctionalEnrichment\")  # extract the gene-sets gs <- geneSet(obj)"},{"path":"/reference/getEvidence.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the scientific evidence for a particular disease-SNP association — getEvidence","title":"Get the scientific evidence for a particular disease-SNP association — getEvidence","text":"function returns biomedical evidence supports association particular SNP phenotypic trait.","code":""},{"path":"/reference/getEvidence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the scientific evidence for a particular disease-SNP association — getEvidence","text":"","code":"getEvidence(variant, diseaseEFO)"},{"path":"/reference/getEvidence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the scientific evidence for a particular disease-SNP association — getEvidence","text":"variant SNP ID particular variant interest (e.g. 'rs394581') diseaseEFO EFO identifier disease interest. can identified function searchDisease()","code":""},{"path":"/reference/getEvidence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the scientific evidence for a particular disease-SNP association — getEvidence","text":"tbl_df dataframe containing information literature evidences disease-SNP association.","code":""},{"path":"/reference/getEvidence.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Get the scientific evidence for a particular disease-SNP association — getEvidence","text":"retrieve evidences disease-SNP association, function makes use gwasrapidd package.","code":""},{"path":"/reference/getEvidence.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Get the scientific evidence for a particular disease-SNP association — getEvidence","text":"Ramiro Magno, Ana-Teresa Maia, gwasrapidd: R package query, download wrangle GWAS catalog data, Bioinformatics, Volume 36, Issue 2, January 2020, Pages 649–650, https://doi.org/10.1093/bioinformatics/btz605.","code":""},{"path":"/reference/getEvidence.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get the scientific evidence for a particular disease-SNP association — getEvidence","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/getEvidence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get the scientific evidence for a particular disease-SNP association — getEvidence","text":"","code":"# \\donttest{ # searchDisease(\"Alzheimer disease\") evidence <- getEvidence(\"rs2075650\", diseaseEFO = \"Alzheimer disease\") #> Retrieving biomedical evidence for the association between Alzheimer disease and rs2075650 variant... #> 109 studies reporting this association were found! # }"},{"path":"/reference/getTargets.html","id":null,"dir":"Reference","previous_headings":"","what":"Get microRNA targets — getTargets","title":"Get microRNA targets — getTargets","text":"function allows obtain human miRNA-target interactions using two databases, namely miRTarBase v9, contains experimentally validated interactions, microRNA Data Integration Portal (mirDIP) database, aggregates miRNA target predictions 24 different resources using integrated score inferred different prediction metrics. way, demonstrated Tokar et al. 2018, mirDIP reports accurate predictions compared individual tools. However, species Homo sapiens validated interactions returned, since mirDIP available human miRNAs.","code":""},{"path":"/reference/getTargets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get microRNA targets — getTargets","text":"","code":"getTargets(   mirnaObj,   organism = \"Homo sapiens\",   score = \"High\",   includeValidated = TRUE )"},{"path":"/reference/getTargets.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get microRNA targets — getTargets","text":"mirnaObj MirnaExperiment object containing miRNA gene data organism specie retrieving miRNA target genes. Available species : Homo sapiens (default), Mus musculus, Rattus norvegicus, Arabidopsis thaliana, Bos taurus, Caenorhabditis elegans, Danio rerio, Drosophila melanogaster, Gallus gallus, Sus scrofa score minimum mirDIP confidence score. must one High, High (default), Medium, Low, correspond ranks among top 1%, top 5% (excluding top 1%), top 1/3 (excluding top 5%) remaining predictions, respectively includeValidated Logical, whether include validated interactions miRTarBase . Default TRUE order retrieve predicted validated targets. Note species Homo sapines validated interactions considered.","code":""},{"path":"/reference/getTargets.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get microRNA targets — getTargets","text":"MirnaExperiment object containing miRNA targets stored targets slot. Results can accessed mirnaTargets() function.","code":""},{"path":"/reference/getTargets.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get microRNA targets — getTargets","text":"define miRNA target genes, can consider experimentally validated computationally predicted interactions. Interactions former type generally preferred, since corroborated biomolecular experiments. However, often sufficient, thus making necessary consider predicted interactions well. downside miRNA target prediction algorithms scarce extend overlap existing different tools. address issue, several ensemble methods developed, trying aggregate predictions obtained different algorithms. Initially, several researchers determined significant miRNA-target pairs predicted one tool (intersection method). However, method able capture important number meaningful interactions. Alternatively, strategies used merge predictions several algorithms (union method). Despite identifying true relationships, union method leads higher proportion false discoveries. Therefore, ensemble methods including mirDIP started using statistics rank miRNA-target predictions obtained multiple algorithms. additional information mirDIP database ranking metric check Tokar et al. 2018 Hauschild et al. 2023. function defines miRNA targets considering validated interactions present miRTarBase (version 9), predicted interactions identified mirDIP. Please note species Homo sapiens, miRTarBase interactions available.","code":""},{"path":"/reference/getTargets.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Get microRNA targets — getTargets","text":"access mirDIP database https://ophid.utoronto.ca/mirDIP/, function directly use mirDIP API R.","code":""},{"path":"/reference/getTargets.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Get microRNA targets — getTargets","text":"Tomas Tokar others, mirDIP 4.1—integrative database human microRNA target predictions, Nucleic Acids Research, Volume 46, Issue D1, 4 January 2018, Pages D360–D370, https://doi.org/10.1093/nar/gkx1144. Anne-Christin Hauschild others, MirDIP 5.2: tissue context annotation novel microRNA curation, Nucleic Acids Research, Volume 51, Issue D1, 6 January 2023, Pages D217–D225, https://doi.org/10.1093/nar/gkac1070. Hsi-Yuan Huang others, miRTarBase update 2022: informative resource experimentally validated miRNA–target interactions, Nucleic Acids Research, Volume 50, Issue D1, 7 January 2022, Pages D222–D230, https://doi.org/10.1093/nar/gkab1079.","code":""},{"path":"/reference/getTargets.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get microRNA targets — getTargets","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/getTargets.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get microRNA targets — getTargets","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # \\donttest{ # retrieve targets obj <- getTargets(mirnaObj = obj) #> Retrieving targets from mirDIP (this may take a while)... #>  Downloading: 5.6 kB      Downloading: 5.6 kB      Downloading: 13 kB      Downloading: 13 kB      Downloading: 14 kB      Downloading: 14 kB      Downloading: 26 kB      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miRNA-target pairs have been identified for the 40 differentially expressed miRNAs. # }  # access targets tg <- mirnaTargets(obj)"},{"path":"/reference/gseaPlot.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a GSEA plot that displays the running enrichment score (ES) for a\ngiven pathway — gseaPlot","title":"Create a GSEA plot that displays the running enrichment score (ES) for a\ngiven pathway — gseaPlot","text":"function creates classic enrichment plot show results gene set enrichment analyses (GSEA). particular, function takes input GSEA results originating enrichGenes() function, returns ggplot2 object GSEA plot. kind plots, running enrichment score (ES) given pathway shown y-axis, whereas gene positions ranked list reported x-axis.","code":""},{"path":"/reference/gseaPlot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a GSEA plot that displays the running enrichment score (ES) for a\ngiven pathway — gseaPlot","text":"","code":"gseaPlot(   enrichment,   pathway,   showTitle = TRUE,   rankingMetric = FALSE,   lineColor = \"green\",   lineSize = 1,   vlineColor = \"red\",   vlineSize = 0.6 )"},{"path":"/reference/gseaPlot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a GSEA plot that displays the running enrichment score (ES) for a\ngiven pathway — gseaPlot","text":"enrichment object class FunctionalEnrichment containing enrichment results pathway must name significantly enriched term/pathway want produce GSEA plot (e.g. 'Thyroid hormone synthesis') showTitle Logical, whether add name pathway/term plot title. Default TRUE rankingMetric Logical, whether show variations ranking metric plot. Default FALSE lineColor must R color name specifies color running score line. Default green. Available color formats include color names, 'blue' 'red', hexadecimal colors specified #RRGGBB lineSize line width running score line. Default 1 vlineColor must R color name specifies color vertical line indicating enrichment score (ES). Default red. Available color formats include color names, 'blue' 'red', hexadecimal colors specified #RRGGBB vlineSize line width vertical line indicating enrichment score (ES). Default 0.6","code":""},{"path":"/reference/gseaPlot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a GSEA plot that displays the running enrichment score (ES) for a\ngiven pathway — gseaPlot","text":"object class ggplot containing GSEA plot.","code":""},{"path":"/reference/gseaPlot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create a GSEA plot that displays the running enrichment score (ES) for a\ngiven pathway — gseaPlot","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/gseaPlot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a GSEA plot that displays the running enrichment score (ES) for a\ngiven pathway — gseaPlot","text":"","code":"# load example FunctionalEnrichment object obj <- loadExamples(\"FunctionalEnrichment\")  # extract results res <- enrichmentResults(obj)  # plot results gseaPlot(obj, pathway = \"Thyroid hormone synthesis\")"},{"path":"/reference/gseaRidgeplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a ridgeplot to display the results of GSEA analysis — gseaRidgeplot","title":"Create a ridgeplot to display the results of GSEA analysis — gseaRidgeplot","text":"function creates ridgeplot useful showing results GSEA analyses. output function plot enriched terms/pathways found enrichGenes() function visualized basis ranking metric used analysis. resulting areas represent density signed p-values, log2 fold changes, log.p-values belonging genes annotated category.","code":""},{"path":"/reference/gseaRidgeplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a ridgeplot to display the results of GSEA analysis — gseaRidgeplot","text":"","code":"gseaRidgeplot(   enrichment,   showTerms = 10,   showTermsParam = \"padj\",   title = NULL )"},{"path":"/reference/gseaRidgeplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a ridgeplot to display the results of GSEA analysis — gseaRidgeplot","text":"enrichment object class FunctionalEnrichment containing enrichment results showTerms number terms shown, based order determined parameter showTermsParam; , alternatively, character vector indicating terms plot. Default 10 showTermsParam order top terms selected specified showTerms parameter. must one ratio, padj (default), pval overlap title title plot. Default NULL include plot title","code":""},{"path":"/reference/gseaRidgeplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a ridgeplot to display the results of GSEA analysis — gseaRidgeplot","text":"object class ggplot containing ridgeplot GSEA results.","code":""},{"path":"/reference/gseaRidgeplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create a ridgeplot to display the results of GSEA analysis — gseaRidgeplot","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/gseaRidgeplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a ridgeplot to display the results of GSEA analysis — gseaRidgeplot","text":"","code":"# load example FunctionalEnrichment object obj <- loadExamples(\"FunctionalEnrichment\")  # extract results res <- enrichmentResults(obj)  # plot results gseaRidgeplot(obj) #> Picking joint bandwidth of 0.561"},{"path":"/reference/integratedPathways.html","id":null,"dir":"Reference","previous_headings":"","what":"Access the results of integrative miRNA-mRNA pathway analyses — integratedPathways","title":"Access the results of integrative miRNA-mRNA pathway analyses — integratedPathways","text":"function accesses data slot FunctionalEnrichment object returns data.frame results integrative topological analysis carried topologicalAnalysis() function.","code":""},{"path":"/reference/integratedPathways.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access the results of integrative miRNA-mRNA pathway analyses — integratedPathways","text":"","code":"integratedPathways(object)"},{"path":"/reference/integratedPathways.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access the results of integrative miRNA-mRNA pathway analyses — integratedPathways","text":"object object class IntegrativePathwayAnalysis containing results miRNA-mRNA pathway analysis","code":""},{"path":"/reference/integratedPathways.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access the results of integrative miRNA-mRNA pathway analyses — integratedPathways","text":"data.frame object containing results topological analysis, returned topologicalAnalysis() function.","code":""},{"path":"/reference/integratedPathways.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Access the results of integrative miRNA-mRNA pathway analyses — integratedPathways","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/integratedPathways.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access the results of integrative miRNA-mRNA pathway analyses — integratedPathways","text":"","code":"# load the example IntegrativePathwayAnalysis object obj <- loadExamples(\"IntegrativePathwayAnalysis\")  # extract results taipaRes <- integratedPathways(obj)"},{"path":"/reference/integration.html","id":null,"dir":"Reference","previous_headings":"","what":"Explore the results of the integration analysis between miRNAs and genes — integration","title":"Explore the results of the integration analysis between miRNAs and genes — integration","text":"performing integration analysis miRNA gene expression values mirnaIntegration() function, results stored integration slot MirnaExperiment object can explored function.","code":""},{"path":"/reference/integration.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Explore the results of the integration analysis between miRNAs and genes — integration","text":"","code":"integration(object, param = FALSE)"},{"path":"/reference/integration.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Explore the results of the integration analysis between miRNAs and genes — integration","text":"object MirnaExperiment object containing miRNA gene data param Logical, whether return complete list object parameters used, just results stored data. Default FALSE","code":""},{"path":"/reference/integration.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Explore the results of the integration analysis between miRNAs and genes — integration","text":"param FALSE, functions returns data.frame object containing results integration analysis. Otherwise, list object including parameters used analysis returned.","code":""},{"path":"/reference/integration.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Explore the results of the integration analysis between miRNAs and genes — integration","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/integration.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Explore the results of the integration analysis between miRNAs and genes — integration","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # perform Kendall's correlation analysis with tau > 0.8 and p < 0.05 obj <- mirnaIntegration(obj,     test = \"correlation\",     corMethod = \"kendall\", corCutoff = 0.8 ) #> As specified by the user, a correlation will be used. #> Performing Kendall's correlation analysis... #> A statistically significant correlation between 1 miRNA-target pairs was found!  # visualize the results of correlation analysis res <- integration(obj) res #>                      microRNA Target microRNA.Direction Corr.Coefficient #> hsa.miR.21.5p.8 hsa-miR-21-5p  MATN2        upregulated       -0.8166667 #>                 Corr.P.Value Corr.Adjusted.P.Val #> hsa.miR.21.5p.8 5.116119e-06         0.003289664"},{"path":"/reference/integrationDatabase.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the database used for integrative miRNA-mRNA pathway analyses — integrationDatabase","title":"Extract the database used for integrative miRNA-mRNA pathway analyses — integrationDatabase","text":"function accesses database slot FunctionalEnrichment object returns name database used topologicalAnalysis() function perform integrative topological analysis.","code":""},{"path":"/reference/integrationDatabase.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the database used for integrative miRNA-mRNA pathway analyses — integrationDatabase","text":"","code":"integrationDatabase(object)"},{"path":"/reference/integrationDatabase.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the database used for integrative miRNA-mRNA pathway analyses — integrationDatabase","text":"object object class IntegrativePathwayAnalysis containing results miRNA-mRNA pathway analysis","code":""},{"path":"/reference/integrationDatabase.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the database used for integrative miRNA-mRNA pathway analyses — integrationDatabase","text":"character object name database used topologicalAnalysis() function, KEGG.","code":""},{"path":"/reference/integrationDatabase.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract the database used for integrative miRNA-mRNA pathway analyses — integrationDatabase","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/integrationDatabase.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the database used for integrative miRNA-mRNA pathway analyses — integrationDatabase","text":"","code":"# load the example IntegrativePathwayAnalysis object obj <- loadExamples(\"IntegrativePathwayAnalysis\")  # see the database integrationDatabase(obj) #> [1] \"KEGG\""},{"path":"/reference/integrationDotplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Display integrated miRNA-mRNA augmented pathways in a dotplot — integrationDotplot","title":"Display integrated miRNA-mRNA augmented pathways in a dotplot — integrationDotplot","text":"function produces dotplot depicts results topologically-aware integrative pathway analysis (TAIPA) carried topologicalAnalysis() function.","code":""},{"path":"/reference/integrationDotplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Display integrated miRNA-mRNA augmented pathways in a dotplot — integrationDotplot","text":"","code":"integrationDotplot(   object,   showTerms = 10,   showTermsParam = \"normalized.score\",   title = NULL )"},{"path":"/reference/integrationDotplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Display integrated miRNA-mRNA augmented pathways in a dotplot — integrationDotplot","text":"object object class IntegrativePathwayAnalysis showTerms number pathways shown, based order determined parameter showTermsParam; , alternatively, character vector indicating pathways plot. Default 10 showTermsParam order top pathways selected specified showTerms parameter. must one coverage, padj, pval, score normalized.score (default) title title plot. Default NULL include plot title","code":""},{"path":"/reference/integrationDotplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Display integrated miRNA-mRNA augmented pathways in a dotplot — integrationDotplot","text":"ggplot graph dotplot integrated pathways.","code":""},{"path":"/reference/integrationDotplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Display integrated miRNA-mRNA augmented pathways in a dotplot — integrationDotplot","text":"producing dotplot function, significant pathways ordered x-axis basis normalized pathway score computed topologicalAnalysis(). higher score, affected pathway biological conditions. Moreover, size dot equal ratio number nodes measurement available, total number nodes (pathway coverage). Finally, color scale dots relative adjusted p-values pathway.","code":""},{"path":"/reference/integrationDotplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Display integrated miRNA-mRNA augmented pathways in a dotplot — integrationDotplot","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/integrationDotplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Display integrated miRNA-mRNA augmented pathways in a dotplot — integrationDotplot","text":"","code":"# load example IntegrativePathwayAnalysis object obj <- loadExamples(\"IntegrativePathwayAnalysis\")  # access the results of pathway analysis integratedPathways(obj) #>                                                                                                 pathway #> Thyroid hormone synthesis                                                     Thyroid hormone synthesis #> Parathyroid hormone synthesis, secretion and action Parathyroid hormone synthesis, secretion and action #> Neurotrophin signaling pathway                                           Neurotrophin signaling pathway #> Cholinergic synapse                                                                 Cholinergic synapse #> GnRH signaling pathway                                                           GnRH signaling pathway #> Estrogen signaling pathway                                                   Estrogen signaling pathway #> Relaxin signaling pathway                                                     Relaxin signaling pathway #>                                                      coverage     score #> Thyroid hormone synthesis                           0.3469388 12.129412 #> Parathyroid hormone synthesis, secretion and action 0.2752294  6.082639 #> Neurotrophin signaling pathway                      0.2362205  5.003782 #> Cholinergic synapse                                 0.2019231  5.357688 #> GnRH signaling pathway                              0.1818182  5.831885 #> Estrogen signaling pathway                          0.2043796  5.117856 #> Relaxin signaling pathway                           0.2388060  5.113116 #>                                                     normalized.score #> Thyroid hormone synthesis                                   9.319461 #> Parathyroid hormone synthesis, secretion and action         4.787852 #> Neurotrophin signaling pathway                              3.123395 #> Cholinergic synapse                                         3.406131 #> GnRH signaling pathway                                      3.784109 #> Estrogen signaling pathway                                  3.450887 #> Relaxin signaling pathway                                   2.984415 #>                                                           P.Val   adj.P.Val #> Thyroid hormone synthesis                           0.000999001 0.000999001 #> Parathyroid hormone synthesis, secretion and action 0.000999001 0.000999001 #> Neurotrophin signaling pathway                      0.006993007 0.006993007 #> Cholinergic synapse                                 0.006993007 0.006993007 #> GnRH signaling pathway                              0.006993007 0.006993007 #> Estrogen signaling pathway                          0.006993007 0.006993007 #> Relaxin signaling pathway                           0.014985015 0.014985015  # create a dotplot of integrated pathways integrationDotplot(obj)"},{"path":"/reference/listPathways.html","id":null,"dir":"Reference","previous_headings":"","what":"List all the available biological pathways in KEGG, Reactome and\nWikiPathways — listPathways","title":"List all the available biological pathways in KEGG, Reactome and\nWikiPathways — listPathways","text":"function can used retrieve list valid biological pathways present KEGG, Reactome WikiPathways.","code":""},{"path":"/reference/listPathways.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"List all the available biological pathways in KEGG, Reactome and\nWikiPathways — listPathways","text":"","code":"listPathways(organism, database)"},{"path":"/reference/listPathways.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"List all the available biological pathways in KEGG, Reactome and\nWikiPathways — listPathways","text":"organism name organism consideration. different databases different supported organisms. see list supported organisms given database, use supportedOrganisms() function database name database use. must one : KEGG, Reactome, WikiPathways","code":""},{"path":"/reference/listPathways.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"List all the available biological pathways in KEGG, Reactome and\nWikiPathways — listPathways","text":"character vector containing pathway names present specified database.","code":""},{"path":"/reference/listPathways.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"List all the available biological pathways in KEGG, Reactome and\nWikiPathways — listPathways","text":"function uses graphite package retrieve biological pathways KEGG, Reactome WikiPathways.","code":""},{"path":"/reference/listPathways.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"List all the available biological pathways in KEGG, Reactome and\nWikiPathways — listPathways","text":"Sales, G., Calura, E., Cavalieri, D. et al. graphite - Bioconductor package convert pathway topology gene network. BMC Bioinformatics 13, 20 (2012), https://doi.org/10.1186/1471-2105-13-20.","code":""},{"path":"/reference/listPathways.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"List all the available biological pathways in KEGG, Reactome and\nWikiPathways — listPathways","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/listPathways.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"List all the available biological pathways in KEGG, Reactome and\nWikiPathways — listPathways","text":"","code":"# list the mouse pathways present in WikiPathways listPathways(\"Mus musculus\", \"WikiPathways\") #>   [1] \"Statin pathway\"                                                                   #>   [2] \"Cholesterol biosynthesis\"                                                         #>   [3] \"Selenium metabolism / selenoproteins\"                                             #>   [4] \"TGF-beta signaling pathway\"                                                       #>   [5] \"Hedgehog signaling pathway\"                                                       #>   [6] \"Glucuronidation\"                                                                  #>   [7] \"EBV LMP1 signaling\"                                                               #>   [8] \"Estrogen signaling\"                                                               #>   [9] \"Transcriptional activation by Nfe2l2 in response to phytochemicals\"               #>  [10] \"Methylation\"                                                                      #>  [11] \"EPO receptor signaling\"                                                           #>  [12] \"Amino acid conjugation of benzoic acid\"                                           #>  [13] \"Type II interferon signaling (IFNG)\"                                              #>  [14] \"Apoptosis\"                                                                        #>  [15] \"Nod-like receptor (NLR) signaling pathway\"                                        #>  [16] \"Retinol metabolism\"                                                               #>  [17] \"ErbB signaling pathway\"                                                           #>  [18] \"Aflatoxin B1 metabolism\"                                                          #>  [19] \"Mitochondrial gene expression\"                                                    #>  [20] \"Estrogen metabolism\"                                                              #>  [21] \"Polyol pathway\"                                                                   #>  [22] \"SIDS susceptibility pathways\"                                                     #>  [23] \"Endochondral ossification\"                                                        #>  [24] \"Selenium micronutrient network\"                                                   #>  [25] \"Folic acid network\"                                                               #>  [26] \"Oxidation by cytochrome P450\"                                                     #>  [27] \"Oxidative damage response\"                                                        #>  [28] \"Dopaminergic neurogenesis\"                                                        #>  [29] \"Regulation of cardiac hypertrophy by miR-208\"                                     #>  [30] \"MicroRNAs in cardiomyocyte hypertrophy\"                                           #>  [31] \"Glycolysis and gluconeogenesis\"                                                   #>  [32] \"Iron homeostasis\"                                                                 #>  [33] \"Cytoplasmic ribosomal proteins\"                                                   #>  [34] \"Glutathione metabolism\"                                                           #>  [35] \"Apoptosis modulation by HSP70\"                                                    #>  [36] \"Acetylcholine synthesis\"                                                          #>  [37] \"Mechanisms associated with pluripotency\"                                          #>  [38] \"One-carbon metabolism and related pathways\"                                       #>  [39] \"Kennedy pathway\"                                                                  #>  [40] \"Heme biosynthesis\"                                                                #>  [41] \"GPCRs, class A rhodopsin-like\"                                                    #>  [42] \"Splicing factor NOVA regulated synaptic proteins\"                                 #>  [43] \"Complement activation, classical pathway\"                                         #>  [44] \"Ptf1a related regulatory pathway\"                                                 #>  [45] \"Hypertrophy model\"                                                                #>  [46] \"Heart development\"                                                                #>  [47] \"Neural crest differentiation\"                                                     #>  [48] \"Alzheimer's disease\"                                                              #>  [49] \"Serotonin receptor 2 and STAT3 signaling\"                                         #>  [50] \"SREBF and miR33 in cholesterol and lipid homeostasis\"                             #>  [51] \"Serotonin and anxiety-related events\"                                             #>  [52] \"Serotonin and anxiety\"                                                            #>  [53] \"BDNF pathway\"                                                                     #>  [54] \"Purine metabolism\"                                                                #>  [55] \"Chemokine signaling pathway\"                                                      #>  [56] \"PPAR signaling pathway\"                                                           #>  [57] \"Fatty acid oxidation\"                                                             #>  [58] \"G protein signaling pathways\"                                                     #>  [59] \"miRNAs and TFs in iPS Cell Generation\"                                            #>  [60] \"Osteoblast signaling\"                                                             #>  [61] \"Spinal cord injury\"                                                               #>  [62] \"Mapk cascade\"                                                                     #>  [63] \"Primary focal segmental glomerulosclerosis (FSGS)\"                                #>  [64] \"Focal adhesion: PI3K-Akt-mTOR signaling pathway\"                                  #>  [65] \"Gene regulatory network modelling somitogenesis\"                                  #>  [66] \"White fat cell differentiation\"                                                   #>  [67] \"Notch signaling pathway\"                                                          #>  [68] \"Electron transport chain\"                                                         #>  [69] \"G13 signaling pathway\"                                                            #>  [70] \"Translation factors\"                                                              #>  [71] \"Glycogen metabolism\"                                                              #>  [72] \"Eicosanoid synthesis\"                                                             #>  [73] \"Fatty acid omega-oxidation\"                                                       #>  [74] \"ESC pluripotency pathways\"                                                        #>  [75] \"p38 Mapk signaling pathway\"                                                       #>  [76] \"ApoE and miR-146 in inflammation and atherosclerosis\"                             #>  [77] \"Tyrobp causal network in microglia\"                                               #>  [78] \"Microglia pathogen phagocytosis pathway\"                                          #>  [79] \"Lung fibrosis\"                                                                    #>  [80] \"Parkinson's disease\"                                                              #>  [81] \"EDA signaling in hair follicle development\"                                       #>  [82] \"Novel Jun-Dmp1 pathway\"                                                           #>  [83] \"BMP signaling pathway in eyelid development\"                                      #>  [84] \"Hfe effect on hepcidin production\"                                                #>  [85] \"Factors and pathways affecting insulin-like growth factor (IGF1)-Akt signaling\"   #>  [86] \"IL-1 signaling pathway\"                                                           #>  [87] \"Prostaglandin synthesis and regulation\"                                           #>  [88] \"Myometrial relaxation and contraction pathways\"                                   #>  [89] \"Wnt signaling in kidney disease\"                                                  #>  [90] \"Robo4 and VEGF signaling pathways crosstalk\"                                      #>  [91] \"ACE inhibitor pathway\"                                                            #>  [92] \"miR-127 in mesendoderm differentiation\"                                           #>  [93] \"Wnt signaling pathway\"                                                            #>  [94] \"Oxidative stress response\"                                                        #>  [95] \"G1 to S cell cycle control\"                                                       #>  [96] \"Distal convoluted tubule 1 (DCT1) cell\"                                           #>  [97] \"Ethanol metabolism resulting in production of ROS by CYP2E1\"                      #>  [98] \"Nuclear receptors in lipid metabolism and toxicity\"                               #>  [99] \"Eicosanoid lipid synthesis map\"                                                   #> [100] \"TCA cycle\"                                                                        #> [101] \"Sphingolipid metabolism overview\"                                                 #> [102] \"Glycerolipids and glycerophospholipids\"                                           #> [103] \"Cholesterol metabolism with Bloch and Kandutsch-Russell pathways\"                 #> [104] \"Eicosanoid metabolism via cyclooxygenases (COX)\"                                  #> [105] \"Eicosanoid metabolism via lipoxygenases (LOX)\"                                    #> [106] \"Eicosanoid metabolism via cytochrome P450 monooxygenases\"                         #> [107] \"One-carbon metabolism\"                                                            #> [108] \"Omega-3 / omega-6 fatty acid synthesis\"                                           #> [109] \"Omega-9 fatty acid synthesis\"                                                     #> [110] \"Oxidative stress and redox pathway\"                                               #> [111] \"Circulating monocytes and cardiac macrophages in diastolic dysfunction\"           #> [112] \"Osteoclast signaling\"                                                             #> [113] \"Inflammatory response pathway\"                                                    #> [114] \"Blood clotting cascade\"                                                           #> [115] \"Lipids measured in liver metastasis from breast cancer\"                           #> [116] \"Sphingolipid metabolism (integrated pathway)\"                                     #> [117] \"Regulation of Pgc1a expression by a Gsk3b-Tfeb signaling axis in skeletal muscle\" #> [118] \"GDNF/RET signaling axis\"                                                          #> [119] \"Peroxiredoxin 2 induced ovarian failure\"                                          #> [120] \"Mapk signaling pathway\"                                                           #> [121] \"Deregulation of renin-angiotensin system by SARS-CoV infection\"                   #> [122] \"Hypoxia-dependent self-renewal of myoblasts\"                                      #> [123] \"Hypoxia-dependent proliferation of myoblasts\"                                     #> [124] \"Hypoxia-dependent differentiation of myoblasts\"                                   #> [125] \"Burn wound healing\"                                                               #> [126] \"Fibrin complement receptor 3 signaling pathway\"                                   #> [127] \"Proteasome degradation\"                                                           #> [128] \"Biogenic amine synthesis\"                                                         #> [129] \"Regulation of actin cytoskeleton\"                                                 #> [130] \"Synthesis and degradation of ketone bodies\"                                       #> [131] \"Exercise-induced circadian regulation\"                                            #> [132] \"Steroid biosynthesis\"                                                             #> [133] \"Calcium regulation in cardiac cells\"                                              #> [134] \"Signal transduction of S1P receptor\"                                              #> [135] \"FAS pathway and stress induction of HSP regulation\"                               #> [136] \"Leptin-insulin signaling overlap\"                                                 #> [137] \"Integrin-mediated cell adhesion\"                                                  #> [138] \"miR-1 in cardiac development\"                                                     #> [139] \"Pentose phosphate pathway\"                                                        #> [140] \"Insulin signaling\"                                                                #> [141] \"Amino acid metabolism\"                                                            #> [142] \"Leptin and adiponectin\"                                                           #> [143] \"Wnt signaling pathway and pluripotency\"                                           #> [144] \"Glutathione and one-carbon metabolism\"                                            #> [145] \"Focal adhesion\"                                                                   #> [146] \"Toll-like receptor signaling\"                                                     #> [147] \"Oxidative phosphorylation\"                                                        #> [148] \"Arachidonate epoxygenase / epoxide hydrolase\"                                     #> [149] \"Metapathway biotransformation\"                                                    #> [150] \"Fatty acid beta-oxidation\"                                                        #> [151] \"Fatty acid biosynthesis\"                                                          #> [152] \"Tryptophan metabolism\"                                                            #> [153] \"Nucleotide GPCRs\"                                                                 #> [154] \"GPCRs, small ligand\"                                                              #> [155] \"Monoamine GPCRs\""},{"path":"/reference/loadExamples.html","id":null,"dir":"Reference","previous_headings":"","what":"Load example MIRit objects — loadExamples","title":"Load example MIRit objects — loadExamples","text":"helper function allows create MirnaExperiment object containing miRNA gene expression data deriving Riesco-Eizaguirre et al (2015), IntegrativePathwayAnalysis object containing TAIPA results dataset, FunctionalEnrichment example GSEA enrichment results.","code":""},{"path":"/reference/loadExamples.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Load example MIRit objects — loadExamples","text":"","code":"loadExamples(class = \"MirnaExperiment\")"},{"path":"/reference/loadExamples.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Load example MIRit objects — loadExamples","text":"class must MirnaExperiment (default) load example object class MirnaExperiment, IntegrativePathwayAnalysis, load example object class IntegrativePathwayAnalysis, FunctionalEnrichment, load example object class FunctionalEnrichment.","code":""},{"path":"/reference/loadExamples.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Load example MIRit objects — loadExamples","text":"example MirnaExperiment object, IntegrativePathwayAnalysis object, FunctionalEnrichment object.","code":""},{"path":"/reference/loadExamples.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Load example MIRit objects — loadExamples","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/loadExamples.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Load example MIRit objects — loadExamples","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # load example IntegrativePathwayAnalysis object obj <- loadExamples(\"IntegrativePathwayAnalysis\")  # load example FunctionalEnrichment object obj <- loadExamples(\"FunctionalEnrichment\")"},{"path":"/reference/mirVariantPlot.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a trackplot to show the association between miRNAs and disease-SNPs — mirVariantPlot","title":"Create a trackplot to show the association between miRNAs and disease-SNPs — mirVariantPlot","text":"function plots trackplot shows genomic position disease-associated SNPs affect miRNA genes. useful visualize genomic position context disease-associated variants may affect miRNA expression.","code":""},{"path":"/reference/mirVariantPlot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a trackplot to show the association between miRNAs and disease-SNPs — mirVariantPlot","text":"","code":"mirVariantPlot(   variantId,   snpAssociation,   showContext = FALSE,   showSequence = TRUE,   snpFill = \"lightblue\",   mirFill = \"orange\",   from = NULL,   to = NULL,   title = NULL,   ... )"},{"path":"/reference/mirVariantPlot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a trackplot to show the association between miRNAs and disease-SNPs — mirVariantPlot","text":"variantId valid name SNP variant! (e.g. \"rs394581\") snpAssociation data.frame object containing results findMirnaSNPs() function showContext Logical, TRUE complete genomic context genes present region shown. Default FALSE just display variant miRNA gene showSequence Logical, whether display color-coded sequence bottom trackplot. Default TRUE. parameter set FALSE showContext TRUE snpFill must R color name specifies fill color SNP locus. Default lightblue. Available color formats include color names, 'blue' 'red', hexadecimal colors specified #RRGGBB mirFill must R color name specifies fill color miRNA locus. Default orange. Available color formats include color names, 'blue' 'red', hexadecimal colors specified #RRGGBB start position plotted genomic range. Default NULL automatically determine appropriate position end position plotted genomic range. Default NULL automatically determine appropriate position title title plot. Default NULL include plot title ... parameters can passed Gviz::plotTracks() function","code":""},{"path":"/reference/mirVariantPlot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a trackplot to show the association between miRNAs and disease-SNPs — mirVariantPlot","text":"trackplot information chromosome, SNP miRNA gene location.","code":""},{"path":"/reference/mirVariantPlot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Create a trackplot to show the association between miRNAs and disease-SNPs — mirVariantPlot","text":"function retrieves genomic coordinates output findMirnaSNPs() function uses Gviz package build trackplot.","code":""},{"path":"/reference/mirVariantPlot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Create a trackplot to show the association between miRNAs and disease-SNPs — mirVariantPlot","text":"Hahne, F., Ivanek, R. (2016). Visualizing Genomic Data Using Gviz Bioconductor. : Mathé, E., Davis, S. (eds) Statistical Genomics. Methods Molecular Biology, vol 1418. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3578-9_16","code":""},{"path":"/reference/mirVariantPlot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create a trackplot to show the association between miRNAs and disease-SNPs — mirVariantPlot","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/mirVariantPlot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a trackplot to show the association between miRNAs and disease-SNPs — mirVariantPlot","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  if (FALSE) { # retrieve associated SNPs association <- findMirnaSNPs(obj, disId)  # visualize association as a trackplot mirVariantPlot(variantId = varId, snpAssociation = association) }"},{"path":"/reference/mirnaCounts.html","id":null,"dir":"Reference","previous_headings":"","what":"Count matrix for microRNA expression in thyroid cancer — mirnaCounts","title":"Count matrix for microRNA expression in thyroid cancer — mirnaCounts","text":"dataset contains gene expression matrix resulting miRNA-Seq analysis thyroid cancer. Specifically, data originate Riesco-Eizaguirre et al (2015), sequenced 8 papillary thyroid carcinomas (PTC) together paired samples normal thyroid tissue. thing done mRNAs order investigate effects target genes. Data included package obtained Gene Expression Omnibus (GEO accession: GSE63511).","code":""},{"path":"/reference/mirnaCounts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count matrix for microRNA expression in thyroid cancer — mirnaCounts","text":"","code":"data(mirnaCounts)"},{"path":[]},{"path":"/reference/mirnaCounts.html","id":"mirnacounts","dir":"Reference","previous_headings":"","what":"mirnaCounts","title":"Count matrix for microRNA expression in thyroid cancer — mirnaCounts","text":"matrix object containing samples columns microRNAs rows.","code":""},{"path":"/reference/mirnaCounts.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Count matrix for microRNA expression in thyroid cancer — mirnaCounts","text":"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63511","code":""},{"path":"/reference/mirnaCounts.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Count matrix for microRNA expression in thyroid cancer — mirnaCounts","text":"Garcilaso Riesco-Eizaguirre et al., “MiR-146b-3p/PAX8/NIS Regulatory Circuit Modulates Differentiation Phenotype Function Thyroid Cells Carcinogenesis,” Cancer Research 75, . 19 (September 30, 2015): 4119–30, https://doi.org/10.1158/0008-5472.CAN-14-3547.","code":""},{"path":"/reference/mirnaIntegration.html","id":null,"dir":"Reference","previous_headings":"","what":"Integrate microRNA and gene expression — mirnaIntegration","title":"Integrate microRNA and gene expression — mirnaIntegration","text":"function allows identify microRNAs significantly associated/correlated targets. principle , since biological role miRNAs mainly negatively regulate gene expression post-transcriptionally, expression microRNA negatively correlated expression targets. test assumption matched-sample data, function performs correlation analysis. hand, unpaired data, offers different one-sided association tests estimate targets -regulated miRNAs enriched -regulated genes vice versa. Additionally, unpaired data, miRNA effects target gene expression can also quantified fast approximation rotation gene-set testing ('fry' method). correlation analyses, default behavior use Spearman's correlation analysis, whereas association tests default option makes use one-sided Boschloo's exact test. See details section information.","code":""},{"path":"/reference/mirnaIntegration.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Integrate microRNA and gene expression — mirnaIntegration","text":"","code":"mirnaIntegration(   mirnaObj,   test = \"auto\",   pCutoff = 0.05,   pAdjustment = \"fdr\",   corMethod = \"spearman\",   corCutoff = 0.5,   associationMethod = \"boschloo\",   nuisanceParam = 100 )"},{"path":"/reference/mirnaIntegration.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Integrate microRNA and gene expression — mirnaIntegration","text":"mirnaObj MirnaExperiment object containing miRNA gene data test statistical test evaluate association miRNAs genes. must one auto (default), automatically determine appropriate statistical test; correlation, perform correlation analysis; association, perform one-sided association test; fry perform integrative analysis rotation gene-set testing pCutoff adjusted p-value cutoff use statistical significance. default value 0.05 pAdjustment p-value correction method multiple testing. must one : fdr (default), BH, none, holm, hochberg, hommel, bonferroni, corMethod correlation method used correlation analysis. must one : spearman (default), pearson, kendall. See details section information corCutoff minimum (negative) value correlation coefficient consider meaningful miRNA-target relationship. Default 0.5 associationMethod statistical test used evaluating association miRNAs targets unpaired data. must one boschloo (default), perform one-sided Boschloo's exact test; fisher-midp, compute one-sided Fisher's exact test Lancaster's mid-p correction; fisher, perform one-sided Fisher's exact test nuisanceParam number nuisance parameter values considered p-value calculation boschloo method. higher value, better p-value estimation accuracy. Default 100","code":""},{"path":"/reference/mirnaIntegration.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Integrate microRNA and gene expression — mirnaIntegration","text":"MirnaExperiment object containing integration results. access results, user can make use integration() function. additional details interpret results miRNA-gene integrative analysis, please see MirnaExperiment.","code":""},{"path":"/reference/mirnaIntegration.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Integrate microRNA and gene expression — mirnaIntegration","text":"already pointed , miRNA gene expression data derive samples, correlation analysis used. evaluating relationships, default method used Spearman's correlation coefficient, : need normally distributed data; assume linearity; much resistant outliers. However, user can also decide use correlation methods, Pearson's Kendall's correlation. Nevertheless, NGS data may happen certain number ties present expression values. can handled spearman method computes tie-corrected version Spearman's coefficients. However, another correlation method suitable perform rank correlation tied data Kendall's tau-b method, usable kendall. Regarding correlation direction, since miRNAs mainly act negative regulators, negatively correlated miRNA-target pairs evaluated, statistical significance calculated one-tailed t-test. Please notice strong batch effects noticed expression data, recommended remove batchCorrection() function implemented MIRit. Moreover, gene expression data miRNA expression data derive different samples (unpaired data), correlation analysis performed. However, one-sided association tests can applied cases evaluate targets -regulated miRNAs statistically enriched -regulated genes, , conversely, targets -regulated miRNAs statistically enriched -regulated genes. case, Fisher's exact test can used assess statistical significance inverse association. Moreover, Lancaster's mid-p adjustment can applied since shown increases statistical power retaining Type error rates. However, Fisher's exact test conditional test requires sum rows columns contingency table fixed. Notably, true genomic data likely different datasets may lead different number DEGs. Therefore, default behavior MIRit use variant Barnard's exact test, named Boschloo's exact test, suitable group sizes contingency tables variable. Moreover, possible demonstrate Boschloo's test uniformly powerful compared Fisher's exact test. Finally, unpaired data, effect DE-miRNAs expression target genes can estimated rotation gene-set tests. particular, fast approximation rotation gene-set testing called fry, implemented limma package, can used statistically quantify influence miRNAs expression changes target genes.","code":""},{"path":"/reference/mirnaIntegration.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Integrate microRNA and gene expression — mirnaIntegration","text":"Ritchie , Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015). “limma powers differential expression analyses RNA-sequencing microarray studies.” Nucleic Acids Research, 43(7), e47. doi:10.1093/nar/gkv007. Di Wu others, ROAST: rotation gene set tests complex microarray experiments, Bioinformatics, Volume 26, Issue 17, September 2010, Pages 2176–2182, https://doi.org/10.1093/bioinformatics/btq401. Routledge, R. D. (1994). Practicing Safe Statistics Mid-p. Canadian Journal Statistics / La Revue Canadienne de Statistique, 22(1), 103–110, https://doi.org/10.2307/3315826. Boschloo R.D. (1970). \"Raised Conditional Level Significance 2x2-table Testing Equality Two Probabilities\". Statistica Neerlandica. 24: 1–35. doi:10.1111/j.1467-9574.1970.tb00104.x.","code":""},{"path":"/reference/mirnaIntegration.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Integrate microRNA and gene expression — mirnaIntegration","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/mirnaIntegration.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Integrate microRNA and gene expression — mirnaIntegration","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # perform integration analysis with default settings obj <- mirnaIntegration(obj) #> Since data derive from paired samples, a correlation test will be used. #> Performing Spearman's correlation analysis... #> A statistically significant correlation between 215 miRNA-target pairs was found!  # perform Kendall's correlation analysis with tau > 0.8 and p < 0.05 obj <- mirnaIntegration(obj,     test = \"correlation\",     corMethod = \"kendall\", corCutoff = 0.8 ) #> As specified by the user, a correlation will be used. #> Performing Kendall's correlation analysis... #> A statistically significant correlation between 1 miRNA-target pairs was found!"},{"path":"/reference/mirnaTargets.html","id":null,"dir":"Reference","previous_headings":"","what":"Explore miRNA-target pairs — mirnaTargets","title":"Explore miRNA-target pairs — mirnaTargets","text":"function accesses targets slot MirnaExperiment object. retrieving miRNA targets getTargets() function, interactions miRNAs target genes stored targets slot can explored function.","code":""},{"path":"/reference/mirnaTargets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Explore miRNA-target pairs — mirnaTargets","text":"","code":"mirnaTargets(object)"},{"path":"/reference/mirnaTargets.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Explore miRNA-target pairs — mirnaTargets","text":"object MirnaExperiment object containing miRNA gene data","code":""},{"path":"/reference/mirnaTargets.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Explore miRNA-target pairs — mirnaTargets","text":"data.frame object containing interactions miRNAs target genes, retrieved getTargets() function.","code":""},{"path":"/reference/mirnaTargets.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Explore miRNA-target pairs — mirnaTargets","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/mirnaTargets.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Explore miRNA-target pairs — mirnaTargets","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # visualize targets targets_df <- mirnaTargets(obj)"},{"path":"/reference/pairedSamples.html","id":null,"dir":"Reference","previous_headings":"","what":"View the relationship between miRNA and gene samples — pairedSamples","title":"View the relationship between miRNA and gene samples — pairedSamples","text":"function allows access pairedSamples slot MirnaExperiment object. MirnaExperiment class able contain miRNA gene expression measurements deriving individuals (paired samples), different subjects (unpaired samples).","code":""},{"path":"/reference/pairedSamples.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"View the relationship between miRNA and gene samples — pairedSamples","text":"","code":"pairedSamples(object)"},{"path":"/reference/pairedSamples.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"View the relationship between miRNA and gene samples — pairedSamples","text":"object MirnaExperiment object containing miRNA gene data","code":""},{"path":"/reference/pairedSamples.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"View the relationship between miRNA and gene samples — pairedSamples","text":"logical value either TRUE paired samples, FALSE unpaired samples.","code":""},{"path":"/reference/pairedSamples.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"View the relationship between miRNA and gene samples — pairedSamples","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/pairedSamples.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"View the relationship between miRNA and gene samples — pairedSamples","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # check if an existing MirnaExperiment object derive from paired samples pairedSamples(obj) #> [1] TRUE"},{"path":"/reference/plotCorrelation.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot correlation between miRNAs and genes within biological groups — plotCorrelation","title":"Plot correlation between miRNAs and genes within biological groups — plotCorrelation","text":"function creates scatter plot shows correlation miRNA gene expression levels. useful correlation analysis performed mirnaIntegration() function, graphically visualize quantitative effect miRNA dysregulations target gene expression. Furthermore, function performs linear/monotonic regression better represent relationships miRNA-target pairs.","code":""},{"path":"/reference/plotCorrelation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot correlation between miRNAs and genes within biological groups — plotCorrelation","text":"","code":"plotCorrelation(   mirnaObj,   mirna,   gene,   condition = NULL,   showCoeff = TRUE,   regression = TRUE,   useRanks = FALSE,   lineCol = \"red\",   lineType = \"dashed\",   lineWidth = 0.8,   pointSize = 3,   colorScale = NULL,   fontSize = 12,   fontFamily = \"\",   legend = \"top\",   borderWidth = 1,   allBorders = TRUE,   grid = TRUE )"},{"path":"/reference/plotCorrelation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot correlation between miRNAs and genes within biological groups — plotCorrelation","text":"mirnaObj MirnaExperiment object containing miRNA gene data mirna name miRNA want observe correlation gene name gene want observe correlation condition must NULL (default) plot expression based group variable used differential expression analysis. Alternatively, must character/factor object specifies group memberships (eg. c(\"healthy, \"healthy\", \"disease\", \"disease\")) showCoeff Logical, whether show correlation coeffficient . Note \"R\" used Pearson's correlation\", \"rho\" Spearman's correlation, \"tau\" Kendall's correlation. Default TRUE regression Logical, whether display linear/monotonic regression line fits miRNA-gene correlation data. Default TRUE useRanks Logical, whether represent non-parametric correlation analyses (Spearman's Kendall's correlations) rank-transformed data. Note case, linear regression performed ranked data instead monotonic regression. Default FALSE lineCol must R color name specifies color regression line. Default red. Available color formats include color names, 'blue' 'red', hexadecimal colors specified #RRGGBB lineType specifies line type used regression line. must either 'blank', 'solid', 'dashed' (default), 'dotted', 'dotdash', 'longdash' 'twodash' lineWidth width fitted regression line (default 0.8) pointSize size points correlation plot (default 3) colorScale must named character vector values correspond R colors, names coincide groups specified condition parameter (eg. c(\"healthy\" = \"green\", \"disease\" = \"red\")). Default NULL, order use default color scale. Available color formats include color names, 'blue' 'red', hexadecimal colors specified #RRGGBB fontSize base size text elements within plot. Default 12 fontFamily base family text elements within plot legend position legend. Allowed values top, bottom, right, left none. default setting top show legend plot. none specified, legend included graph. borderWidth width plot borders (default 1) allBorders Logical, whetether show panel borders, just bottom left borders. Default TRUE grid Logical, whether show grid lines . Default TRUE","code":""},{"path":"/reference/plotCorrelation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot correlation between miRNAs and genes within biological groups — plotCorrelation","text":"object class ggplot containing correlation scatter plot.","code":""},{"path":"/reference/plotCorrelation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot correlation between miRNAs and genes within biological groups — plotCorrelation","text":"non-parametric correlation performed mirnaIntegration() function, regression line can fitted monotonic regression expression levels, linear regression performed rank-transformed data. Since, ranks correspond real expression values, default option perform monotonic regression fit monotonic curve. , function makes use MonoPoly R package, implements algorithm proposed Murray et al. 2016.","code":""},{"path":"/reference/plotCorrelation.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Plot correlation between miRNAs and genes within biological groups — plotCorrelation","text":"K. Murray, S. Müller & B. . Turlach (2016) Fast flexible methods monotone polynomial fitting, Journal Statistical Computation Simulation, 86:15, 2946-2966, DOI: 10.1080/00949655.2016.1139582.","code":""},{"path":"/reference/plotCorrelation.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot correlation between miRNAs and genes within biological groups — plotCorrelation","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/plotCorrelation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot correlation between miRNAs and genes within biological groups — plotCorrelation","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # perform miRNA-target integration obj <- mirnaIntegration(obj) #> Since data derive from paired samples, a correlation test will be used. #> Performing Spearman's correlation analysis... #> A statistically significant correlation between 215 miRNA-target pairs was found!  # plot correlation between miR-146b and PAX8 with monotonic regression curve plotCorrelation(obj, \"hsa-miR-146b-5p\", \"PAX8\", condition = \"disease\")"},{"path":"/reference/plotDE.html","id":null,"dir":"Reference","previous_headings":"","what":"Represent differentially expressed miRNAs/genes as boxplots, barplots or\nviolinplots — plotDE","title":"Represent differentially expressed miRNAs/genes as boxplots, barplots or\nviolinplots — plotDE","text":"function able produce boxplots, barplots violinplots useful visualize miRNA gene differential expression. user just provide vector interesting miRNA/genes wants plot (e.g. \"hsa-miR-34a-5p\", \"hsa-miR-146b-5p\", \"PAX8\"). chart type can specified graph parameter.","code":""},{"path":"/reference/plotDE.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Represent differentially expressed miRNAs/genes as boxplots, barplots or\nviolinplots — plotDE","text":"","code":"plotDE(   mirnaObj,   features,   condition = NULL,   graph = \"boxplot\",   linear = TRUE,   showSignificance = TRUE,   starSig = TRUE,   pCol = \"adj.P.Val\",   sigLabelSize = 7,   digits = 3,   nameAsTitle = FALSE,   colorScale = NULL,   fontSize = 12,   fontFamily = \"\",   legend = \"top\",   borderWidth = 1,   allBorders = FALSE,   grid = FALSE )"},{"path":"/reference/plotDE.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Represent differentially expressed miRNAs/genes as boxplots, barplots or\nviolinplots — plotDE","text":"mirnaObj MirnaExperiment object containing miRNA gene data features character vector containing genes/miRNAs plot condition must NULL (default) plot expression based group variable used differential expression analysis. Alternatively, must character/factor object specifies group memberships (eg. c(\"healthy, \"healthy\", \"disease\", \"disease\")) graph type plot produce. must one boxplot (default), barplot, violinplot linear Logical, whether plot expression levels linear scale log2 space. Default TRUE order use linear space showSignificance Logical, whether display statistical significance . Default TRUE starSig Logical, whether represent statistical significance stars. Default TRUE, significance scale : * \\(p < 0.05\\), ** \\(p < 0.01\\), *** \\(p < 0.001\\), **** \\(p < 0.0001\\). starSig set FALSE, p-values adjusted p-values reported plot numbers pCol statistics used evaluate comparison significance. must one P.Value, use unadjusted p-values, adj.P.Val (default), use p-values corrected multiple testing sigLabelSize size labels used show statistical significance. Default 7, well suited representing p-values significance stars. However, starSig set FALSE, user might downsize parameter digits number digits show p-values reported numbers (starSig FALSE). Default 3 nameAsTitle Logical, set TRUE, miRNA/gene name added plot title, x-axis legend removed. Note option considered features contains just one miRNA/gene. Default FALSE colorScale must named character vector values correspond R colors, names coincide groups specified condition parameter (eg. c(\"healthy\" = \"green\", \"disease\" = \"red\")). Default NULL, order use default color scale. Available color formats include color names, 'blue' 'red', hexadecimal colors specified #RRGGBB fontSize base size text elements within plot. Default 12 fontFamily base family text elements within plot legend position legend. Allowed values top, bottom, right, left none. default setting top show legend plot. none specified, legend included graph. borderWidth width plot borders (default 1) allBorders Logical, whetether show panel borders, just bottom left borders. Default FALSE grid Logical, whether show grid lines . Default FALSE","code":""},{"path":"/reference/plotDE.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Represent differentially expressed miRNAs/genes as boxplots, barplots or\nviolinplots — plotDE","text":"object class ggplot containing plot.","code":""},{"path":"/reference/plotDE.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Represent differentially expressed miRNAs/genes as boxplots, barplots or\nviolinplots — plotDE","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/plotDE.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Represent differentially expressed miRNAs/genes as boxplots, barplots or\nviolinplots — plotDE","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # produce a boxplot for PAX8 and miR-34a-5p plotDE(obj, features = c(\"hsa-miR-34a-5p\", \"PAX8\"))   # produce a barplot for PAX8 and miR-34a-5p without significance plotDE(obj,     features = c(\"hsa-miR-34a-5p\", \"PAX8\"),     graph = \"barplot\", showSignificance = FALSE ) #> Warning: Computation failed in `stat_summary()` #> Caused by error in `get()`: #> ! object 'mean_sd' of mode 'function' was not found   # produce a violinplot for BCL2 plotDE(obj, features = \"BCL2\", graph = \"violinplot\") #> Warning: Removed 77 rows containing missing values (`geom_violin()`)."},{"path":"/reference/plotDimensions.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate multidimensional scaling (MDS) plots to explore miRNA/gene\nexpression distances — plotDimensions","title":"Generate multidimensional scaling (MDS) plots to explore miRNA/gene\nexpression distances — plotDimensions","text":"function performs multidimensional scaling order produce simple scatterplot shows miRNA/gene expression variations among samples. particular, starting MirnaExperiment object, functions allows visualize miRNA gene expression multidimensional space. Moreover, possible color samples basis specific variables, extremely useful assess miRNA/gene expression variations distinct biological groups.","code":""},{"path":"/reference/plotDimensions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate multidimensional scaling (MDS) plots to explore miRNA/gene\nexpression distances — plotDimensions","text":"","code":"plotDimensions(   mirnaObj,   assay,   condition = NULL,   dimensions = c(1, 2),   labels = FALSE,   boxedLabel = TRUE,   pointSize = 3,   pointAlpha = 1,   colorScale = NULL,   title = NULL,   fontSize = 12,   fontFamily = \"\",   legend = \"top\",   borderWidth = 1,   allBorders = TRUE,   grid = FALSE,   ... )"},{"path":"/reference/plotDimensions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate multidimensional scaling (MDS) plots to explore miRNA/gene\nexpression distances — plotDimensions","text":"mirnaObj MirnaExperiment object containing miRNA gene data assay results display. must either 'microRNA', plot miRNA expression, 'genes', produce MDS plot genes condition must column name variable specified metadata (colData) MirnaExperiment object; , alternatively, must character/factor object specifies group memberships (eg. c(\"healthy, \"healthy\", \"disease\", \"disease\")) dimensions numeric vector length 2 indicates two dimensions represent plot. Default c(1, 2) plot two dimensions account highest portion variability labels Logical, whether display labels . Default FALSE boxedLabel Logical, whether show labels inside rectangular shape (default) just text elements pointSize size points MDS plot (default 3) pointAlpha transparency points MDS plot (default 1) colorScale must named character vector values correspond R colors, names coincide groups specified condition parameter (eg. c(\"healthy\" = \"green\", \"disease\" = \"red\")). Default NULL, order use default color scale. Available color formats include color names, 'blue' 'red', hexadecimal colors specified #RRGGBB title title plot. Default NULL include plot title fontSize base size text elements within plot. Default 12 fontFamily base family text elements within plot legend position legend. Allowed values top, bottom, right, left none. default setting top show legend plot. none specified, legend included graph. borderWidth width plot borders (default 1) allBorders Logical, whetether show panel borders, just bottom left borders. Default TRUE grid Logical, whether show grid lines . Default FALSE ... parameters can passed limma::plotMDS() function","code":""},{"path":"/reference/plotDimensions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate multidimensional scaling (MDS) plots to explore miRNA/gene\nexpression distances — plotDimensions","text":"object class ggplot containing plot.","code":""},{"path":"/reference/plotDimensions.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Generate multidimensional scaling (MDS) plots to explore miRNA/gene\nexpression distances — plotDimensions","text":"perform multidimensional scaling, function internally uses limma::plotMDS() function provided limma package.","code":""},{"path":"/reference/plotDimensions.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generate multidimensional scaling (MDS) plots to explore miRNA/gene\nexpression distances — plotDimensions","text":"Ritchie , Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015). “limma powers differential expression analyses RNA-sequencing microarray studies.” Nucleic Acids Research, 43(7), e47. doi:10.1093/nar/gkv007.","code":""},{"path":"/reference/plotDimensions.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate multidimensional scaling (MDS) plots to explore miRNA/gene\nexpression distances — plotDimensions","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/plotDimensions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate multidimensional scaling (MDS) plots to explore miRNA/gene\nexpression distances — plotDimensions","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # produce MDS plot for miRNA expression with labels plotDimensions(obj, \"microRNA\", condition = \"disease\", labels = TRUE)   # produce MDS plot for genes without condition color plotDimensions(obj, \"genes\")"},{"path":"/reference/plotVolcano.html","id":null,"dir":"Reference","previous_headings":"","what":"Produce volcano plots to display miRNA/gene differential expression — plotVolcano","title":"Produce volcano plots to display miRNA/gene differential expression — plotVolcano","text":"function allows user create publication-quality volcano plots represent results miRNA/gene differential expression. kind plots, x-axis relative log2 fold change biological conditions, y-axis contains negative base-10 logarithm p-value. Note, even volcano plots display unadjusted p-values y-axis, cutoff level shown plot derive adjusted p-value cutoff used differential expression analysis.","code":""},{"path":"/reference/plotVolcano.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Produce volcano plots to display miRNA/gene differential expression — plotVolcano","text":"","code":"plotVolcano(   mirnaObj,   assay,   labels = NULL,   boxedLabel = TRUE,   pointSize = 3,   pointAlpha = 0.4,   interceptWidth = 0.6,   interceptColor = \"black\",   interceptType = \"dashed\",   colorScale = c(\"blue\", \"grey\", \"red\"),   title = NULL,   fontSize = 12,   fontFamily = \"\",   legend = \"none\",   borderWidth = 1,   allBorders = TRUE,   grid = FALSE )"},{"path":"/reference/plotVolcano.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Produce volcano plots to display miRNA/gene differential expression — plotVolcano","text":"mirnaObj MirnaExperiment object containing miRNA gene data assay results display. must either 'microRNA', plot miRNA differential expression, 'genes', show results genes labels labels show graph. Default NULL include labels. parameter can character vector containing IDs features want display. Alternatively, parameter can also number significant features want plot labels boxedLabel Logical, whether show labels inside rectangular shape (default) just text elements pointSize size points volcano plot (default 3) pointAlpha transparency points volcano plot (default 0.4) interceptWidth width cutoff intercepts (default 0.6) interceptColor must R color name specifies color cutoff intercepts. Default black. Available color formats include color names, 'blue' 'red', hexadecimal colors specified #RRGGBB interceptType specifies line type used cutoff intercepts. must either 'blank', 'solid', 'dashed' (default), 'dotted', 'dotdash', 'longdash' 'twodash' colorScale must character vector length 3 containing valid R color names downregulated, non significant, upregulated features, respectively. Default value c('blue', 'grey', 'red'). Available color formats include color names, 'blue' 'red', hexadecimal colors specified #RRGGBB title title plot. Default NULL include plot title fontSize base size text elements within plot. Default 12 fontFamily base family text elements within plot legend position legend. Allowed values top, bottom, right, left none. default setting none legend included graph. borderWidth width plot borders (default 1) allBorders Logical, whetether show panel borders, just bottom left borders. Default TRUE grid Logical, whether show grid lines . Default FALSE","code":""},{"path":"/reference/plotVolcano.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Produce volcano plots to display miRNA/gene differential expression — plotVolcano","text":"object class ggplot containing plot.","code":""},{"path":"/reference/plotVolcano.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Produce volcano plots to display miRNA/gene differential expression — plotVolcano","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/plotVolcano.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Produce volcano plots to display miRNA/gene differential expression — plotVolcano","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # produce a volcano plot for miRNAs with labels plotVolcano(obj, \"microRNA\", labels = 5)   # produce a volcano plot for genes plotVolcano(obj, \"genes\")"},{"path":"/reference/preparePathways.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare miRNA-augmented pathways for integrative miRNA-mRNA pathway analyses — preparePathways","title":"Prepare miRNA-augmented pathways for integrative miRNA-mRNA pathway analyses — preparePathways","text":"function takes influential miRNA-mRNA interactions, identified mirnaIntegration() function, adds biological pathways retrieved pathway database KEGG, WikiPathways Reactome. pathways returned function needed perform topologically-aware integrative pathway analysis (TAIPA) topologicalAnalysis() function.","code":""},{"path":"/reference/preparePathways.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare miRNA-augmented pathways for integrative miRNA-mRNA pathway analyses — preparePathways","text":"","code":"preparePathways(   mirnaObj,   database = \"KEGG\",   organism = \"Homo sapiens\",   minPc = 10,   BPPARAM = bpparam() )"},{"path":"/reference/preparePathways.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare miRNA-augmented pathways for integrative miRNA-mRNA pathway analyses — preparePathways","text":"mirnaObj MirnaExperiment object containing miRNA gene data database name database use. must one : KEGG, Reactome, WikiPathways. Default KEGG organism name organism consideration. different databases different supported organisms. see list supported organisms given database, use supportedOrganisms() function. Default specie Homo sapiens minPc minimum percentage measured features pathway must considered analysis. Default 10. See details section additional information BPPARAM desired parallel computing behavior. parameter defaults BiocParallel::bpparam(), can edited. See BiocParallel::bpparam() information parallel computing R","code":""},{"path":"/reference/preparePathways.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare miRNA-augmented pathways for integrative miRNA-mRNA pathway analyses — preparePathways","text":"list object containing miRNA-augmented pathways graphNEL objects.","code":""},{"path":"/reference/preparePathways.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Prepare miRNA-augmented pathways for integrative miRNA-mRNA pathway analyses — preparePathways","text":"create augmented pathways, function uses graphite R package download biological networks mentioned databases. , pathway converted graph object, significant miRNA-mRNA interactions added network. , edge weights added according interaction type. point, biological pathways nodes measured excluded analysis. required , differential expression analysis, lowly expressed features removed. Therefore, pathways might result significantly affected even 1% nodes perturbed. default behavior exclude pathways less 10% representation (minPc = 10). Finally, function performs breadth-first search (BFS) algorithm topologically sort pathway nodes individual node occurs upstream nodes. Nodes within cycles considered leaf nodes. Information pathway coverage, .e. percentage nodes expression measurments, edge weights, topological sorting order, parameters used create networks stored graphData slot graphNEL object.","code":""},{"path":"/reference/preparePathways.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Prepare miRNA-augmented pathways for integrative miRNA-mRNA pathway analyses — preparePathways","text":"Sales, G., Calura, E., Cavalieri, D. et al. graphite - Bioconductor package convert pathway topology gene network. BMC Bioinformatics 13, 20 (2012), https://doi.org/10.1186/1471-2105-13-20.","code":""},{"path":"/reference/preparePathways.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Prepare miRNA-augmented pathways for integrative miRNA-mRNA pathway analyses — preparePathways","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/preparePathways.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prepare miRNA-augmented pathways for integrative miRNA-mRNA pathway analyses — preparePathways","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # perform integration analysis with default settings obj <- mirnaIntegration(obj) #> Since data derive from paired samples, a correlation test will be used. #> Performing Spearman's correlation analysis... #> A statistically significant correlation between 215 miRNA-target pairs was found!  # \\donttest{ # retrieve pathways from KEGG and augment them with miRNA-gene interactions paths <- preparePathways(obj) #> Downloading pathways from KEGG database... #> Converting identifiers to gene symbols... #> Adding miRNA-gene interactions to biological pathways... #> Warning: 155 pathways have been ignored because they contain too few nodes with gene expression measurement. #> Performing topological sorting of pathway nodes...  # perform the integrative pathway analysis with 1000 permutations ipa <- topologicalAnalysis(obj, paths, nPerm = 1000) #> Calculating pathway scores... #> Generating random permutations... #> Calculating p-values with 1000 permutations... #> Correcting p-values through the max-T procedure... #> The topologically-aware integrative pathway analysis reported 2 significantly altered pathways!  # access the results of pathway analysis integratedPathways(ipa) #>                                             pathway  coverage    score #> Thyroid hormone synthesis Thyroid hormone synthesis 0.3469388 12.12941 #> Thyroid cancer                       Thyroid cancer 0.2820513 11.56291 #>                           normalized.score       P.Val adj.P.Val #> Thyroid hormone synthesis         8.287455 0.000999001     0.017 #> Thyroid cancer                    7.396507 0.000999001     0.042  # create a dotplot of integrated pathways integrationDotplot(ipa)   # explore a specific biological network visualizeNetwork(ipa, \"Thyroid hormone synthesis\")  # }"},{"path":"/reference/searchDisease.html","id":null,"dir":"Reference","previous_headings":"","what":"Search for disease EFO identifiers — searchDisease","title":"Search for disease EFO identifiers — searchDisease","text":"function allows retrieve Experimental Factor Ontology (EFO) identifier particular disease. ID needed use function findMirnaSNPs().","code":""},{"path":"/reference/searchDisease.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Search for disease EFO identifiers — searchDisease","text":"","code":"searchDisease(diseaseName)"},{"path":"/reference/searchDisease.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Search for disease EFO identifiers — searchDisease","text":"diseaseName name particular disease (ex. Alzheimer disease).","code":""},{"path":"/reference/searchDisease.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Search for disease EFO identifiers — searchDisease","text":"character object containing EFO identifiers.","code":""},{"path":"/reference/searchDisease.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Search for disease EFO identifiers — searchDisease","text":"retrieve EFO IDs specific diseases, function makes use gwasrapidd package.","code":""},{"path":"/reference/searchDisease.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Search for disease EFO identifiers — searchDisease","text":"Ramiro Magno, Ana-Teresa Maia, gwasrapidd: R package query, download wrangle GWAS catalog data, Bioinformatics, Volume 36, Issue 2, January 2020, Pages 649–650, https://doi.org/10.1093/bioinformatics/btz605.","code":""},{"path":"/reference/searchDisease.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Search for disease EFO identifiers — searchDisease","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/searchDisease.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Search for disease EFO identifiers — searchDisease","text":"","code":"# \\donttest{ # search the EFO identifier of Alzheimer disease searchDisease(\"Alzheimer disease\") #> Checking for cached EFO traits... #> Reading EFO traits from cache... #> Searching for disease: Alzheimer disease #> [1] \"Alzheimer's disease biomarker measurement\"  #> [2] \"Alzheimer's disease neuropathologic change\" #> [3] \"Alzheimer disease\"                          #> [4] \"late-onset Alzheimers disease\"              #> [5] \"family history of Alzheimer’s disease\"      #> [6] \"age of onset of Alzheimer disease\"          # }"},{"path":"/reference/significantAccessors.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the IDs of statistically differentially expressed miRNAs/genes — significantAccessors","title":"Get the IDs of statistically differentially expressed miRNAs/genes — significantAccessors","text":"significantMirnas() significantGenes() functions access significant features contained mirnaDE geneDE slots MirnaExperiment object, can used obtain IDs statistically differentially expressed miRNAs genes.","code":""},{"path":"/reference/significantAccessors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the IDs of statistically differentially expressed miRNAs/genes — significantAccessors","text":"","code":"significantMirnas(object)  significantGenes(object)"},{"path":"/reference/significantAccessors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the IDs of statistically differentially expressed miRNAs/genes — significantAccessors","text":"object MirnaExperiment object containing miRNA gene data","code":""},{"path":"/reference/significantAccessors.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the IDs of statistically differentially expressed miRNAs/genes — significantAccessors","text":"character vector miRNA IDs (e.g. 'hsa-miR-16-5p', hsa-miR-29a-3p'...), acharacter vector gene symbols (e.g. 'TP53', 'FOXP2', 'TIGAR', CASP1'...).","code":""},{"path":"/reference/significantAccessors.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Get the IDs of statistically differentially expressed miRNAs/genes — significantAccessors","text":"significantMirnas(): Get IDs differentially expressed miRNAs significantGenes(): Get IDs differentially expressed genes","code":""},{"path":"/reference/significantAccessors.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get the IDs of statistically differentially expressed miRNAs/genes — significantAccessors","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/significantAccessors.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get the IDs of statistically differentially expressed miRNAs/genes — significantAccessors","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # extract significant DE-miRNAs sigMirnas <- significantMirnas(obj)  # extract significant DEGs sigGenes <- significantGenes(obj)"},{"path":"/reference/supportedOrganisms.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the list of supported organisms for a given database — supportedOrganisms","title":"Get the list of supported organisms for a given database — supportedOrganisms","text":"function provides list supported organisms different databases, namely Gene Ontology (GO), Kyoto Encyclopedia Genes Genomes (KEGG), MsigDB, WikiPathways, Reactome, Enrichr, Disease Ontology (), Network Cancer Genes (NCG), DisGeNET, COVID19.","code":""},{"path":"/reference/supportedOrganisms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the list of supported organisms for a given database — supportedOrganisms","text":"","code":"supportedOrganisms(database)"},{"path":"/reference/supportedOrganisms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the list of supported organisms for a given database — supportedOrganisms","text":"database database name. must one : GO, KEGG, MsigDB, WikiPathways, Reactome, Enrichr, , NCG, DisGeNET, COVID19","code":""},{"path":"/reference/supportedOrganisms.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the list of supported organisms for a given database — supportedOrganisms","text":"character vector listing supported organisms database specified user.","code":""},{"path":"/reference/supportedOrganisms.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Get the list of supported organisms for a given database — supportedOrganisms","text":"perform functional enrichment genes, MIRit uses geneset R package download gene sets mentioned databases.","code":""},{"path":"/reference/supportedOrganisms.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Get the list of supported organisms for a given database — supportedOrganisms","text":"Liu, Y., Li, G. Empowering biologists decode omics data: Genekitr R package web server. BMC Bioinformatics 24, 214 (2023). https://doi.org/10.1186/s12859-023-05342-9.","code":""},{"path":"/reference/supportedOrganisms.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get the list of supported organisms for a given database — supportedOrganisms","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/supportedOrganisms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get the list of supported organisms for a given database — supportedOrganisms","text":"","code":"# get the supported organisms for GO database supportedOrganisms(\"GO\") #>   [1] \"Amborella trichopoda\"               \"Anolis carolinensis\"                #>   [3] \"Anopheles gambiae\"                  \"Aquifex aeolicus\"                   #>   [5] \"Arabidopsis thaliana\"               \"Ashbya gossypii\"                    #>   [7] \"Bacillus cereus\"                    \"Bacillus subtilis\"                  #>   [9] \"Bacteroides thetaiotaomicron\"       \"Batrachochytrium dendrobatidis\"     #>  [11] \"Bos taurus\"                         \"Brachypodium distachyon\"            #>  [13] \"Bradyrhizobium diazoefficiens\"      \"Branchiostoma floridae\"             #>  [15] \"Brassica napus\"                     \"Brassica rapa subsp. pekinensis\"    #>  [17] \"Caenorhabditis briggsae\"            \"Caenorhabditis elegans\"             #>  [19] \"Candida albicans\"                   \"Canis lupus familiaris\"             #>  [21] \"Capsicum annuum\"                    \"Chlamydia trachomatis\"              #>  [23] \"Chlamydomonas reinhardtii\"          \"Chloroflexus aurantiacus\"           #>  [25] \"Ciona intestinalis\"                 \"Citrus sinensis\"                    #>  [27] \"Clostridium botulinum\"              \"Coxiella burnetii\"                  #>  [29] \"Cryptococcus neoformans\"            \"Cucumis sativus\"                    #>  [31] \"Danio rerio\"                        \"Daphnia pulex\"                      #>  [33] \"Deinococcus radiodurans\"            \"Dictyoglomus turgidum\"              #>  [35] \"Dictyostelium discoideum\"           \"Dictyostelium purpureum\"            #>  [37] \"Drosophila melanogaster\"            \"Emericella nidulans\"                #>  [39] \"Entamoeba histolytica\"              \"Equus caballus\"                     #>  [41] \"Erythranthe guttata\"                \"Escherichia coli\"                   #>  [43] \"Eucalyptus grandis\"                 \"Felis catus\"                        #>  [45] \"Fusobacterium nucleatum\"            \"Gallus gallus\"                      #>  [47] \"Geobacter sulfurreducens\"           \"Giardia intestinalis\"               #>  [49] \"Gloeobacter violaceus\"              \"Glycine max\"                        #>  [51] \"Gorilla gorilla gorilla\"            \"Gossypium hirsutum\"                 #>  [53] \"Haemophilus influenzae\"             \"Halobacterium salinarum\"            #>  [55] \"Helianthus annuus\"                  \"Helicobacter pylori\"                #>  [57] \"helobdella robusta\"                 \"Homo sapiens\"                       #>  [59] \"Hordeum vulgare subsp. vulgare\"     \"Ixodes scapularis\"                  #>  [61] \"Juglans regia\"                      \"Klebsormidium nitens\"               #>  [63] \"Korarchaeum cryptofilum\"            \"Lactuca sativa\"                     #>  [65] \"Leishmania major\"                   \"lepisosteus oculatus\"               #>  [67] \"Leptospira interrogans\"             \"Listeria monocytogenes\"             #>  [69] \"Macaca mulatta\"                     \"Manihot esculenta\"                  #>  [71] \"Marchantia polymorpha\"              \"Medicago truncatula\"                #>  [73] \"Methanocaldococcus jannaschii\"      \"Methanosarcina acetivorans\"         #>  [75] \"Monodelphis domestica\"              \"Monosiga brevicollis\"               #>  [77] \"Mus musculus\"                       \"Musa acuminata subsp. malaccensis\"  #>  [79] \"Mycobacterium tuberculosis\"         \"mycoplasma genitalium\"              #>  [81] \"Neisseria meningitidis serogroup b\" \"Nelumbo nucifera\"                   #>  [83] \"Nematostella vectensis\"             \"Neosartorya fumigata\"               #>  [85] \"Neurospora crassa\"                  \"Nicotiana tabacum\"                  #>  [87] \"Nitrosopumilus maritimus\"           \"Ornithorhynchus anatinus\"           #>  [89] \"Oryza sativa\"                       \"Oryzias latipes\"                    #>  [91] \"Pan troglodytes\"                    \"Paramecium tetraurelia\"             #>  [93] \"Phaeosphaeria nodorum\"              \"Physcomitrella patens\"              #>  [95] \"Phytophthora ramorum\"               \"Plasmodium falciparum\"              #>  [97] \"Populus trichocarpa\"                \"Pristionchus pacificus\"             #>  [99] \"Prunus persica\"                     \"Pseudomonas aeruginosa\"             #> [101] \"Puccinia graminis\"                  \"Pyrobaculum aerophilum\"             #> [103] \"Rattus norvegicus\"                  \"Rhodopirellula baltica\"             #> [105] \"Ricinus communis\"                   \"Saccharomyces cerevisiae\"           #> [107] \"Salmonella typhimurium\"             \"Schizosaccharomyces japonicus\"      #> [109] \"Schizosaccharomyces pombe\"          \"Sclerotinia sclerotiorum\"           #> [111] \"Selaginella moellendorffii\"         \"Setaria italica\"                    #> [113] \"Shewanella oneidensis\"              \"Solanum lycopersicum\"               #> [115] \"Solanum tuberosum\"                  \"Sorghum bicolor\"                    #> [117] \"Spinacia oleracea\"                  \"Staphylococcus aureus\"              #> [119] \"Streptococcus pneumoniae\"           \"Streptomyces coelicolor\"            #> [121] \"Strongylocentrotus purpuratus\"      \"Sulfolobus solfataricus\"            #> [123] \"Sus scrofa\"                         \"Synechocystis\"                      #> [125] \"Thalassiosira pseudonana\"           \"Theobroma cacao\"                    #> [127] \"Thermococcus kodakaraensis\"         \"Thermodesulfovibrio yellowstonii\"   #> [129] \"Thermotoga maritima\"                \"Tribolium castaneum\"                #> [131] \"Trichomonas vaginalis\"              \"Trichoplax adhaerens\"               #> [133] \"Triticum aestivum\"                  \"Trypanosoma brucei\"                 #> [135] \"Ustilago maydis\"                    \"Vibrio cholerae\"                    #> [137] \"Vitis vinifera\"                     \"Xanthomonas campestris\"             #> [139] \"Xenopus tropicalis\"                 \"Yarrowia lipolytica\"                #> [141] \"Yersinia pestis\"                    \"Zea mays\"                           #> [143] \"Zostera marina\"                      # get the supported organisms for Reactome supportedOrganisms(\"Reactome\") #>  [1] \"Bos taurus\"               \"Caenorhabditis elegans\"   #>  [3] \"Danio rerio\"              \"Drosophila melanogaster\"  #>  [5] \"Gallus gallus\"            \"Homo sapiens\"             #>  [7] \"Mus musculus\"             \"Rattus norvegicus\"        #>  [9] \"Saccharomyces cerevisiae\" \"Sus scrofa\"               #> [11] \"Xenopus tropicalis\""},{"path":"/reference/topologicalAnalysis.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform a topologically-aware integrative pathway analysis (TAIPA) — topologicalAnalysis","title":"Perform a topologically-aware integrative pathway analysis (TAIPA) — topologicalAnalysis","text":"function allows perform integrative pathway analysis aims identify biological networks affected miRNomic transcriptomic dysregulations. function takes miRNA-augmented pathways, created preparePathways() function, calculates score estimates degree impairment pathway. Later, statistical significance calculated permutation test. main advantages method require matched samples, allows perform integrative miRNA-mRNA pathway analysis takes account topology biological networks. See details section additional information.","code":""},{"path":"/reference/topologicalAnalysis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform a topologically-aware integrative pathway analysis (TAIPA) — topologicalAnalysis","text":"","code":"topologicalAnalysis(   mirnaObj,   pathways,   pCutoff = 0.05,   pAdjustment = \"max-T\",   nPerm = 10000,   progress = FALSE,   tasks = 0,   BPPARAM = bpparam() )"},{"path":"/reference/topologicalAnalysis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform a topologically-aware integrative pathway analysis (TAIPA) — topologicalAnalysis","text":"mirnaObj MirnaExperiment object containing miRNA gene data pathways list miRNA-augmented pathways returned preparePathways() function pCutoff adjusted p-value cutoff use statistical significance. default value 0.05 pAdjustment p-value correction method multiple testing. must one : max-T (default), fdr, BH, none, holm, hochberg, hommel, bonferroni, nPerm number permutation used assessing statistical significance pathway. Default 10000. See details section additional information progress Logical, whether show progress bar p-value calculation . Default FALSE, include progress bar. Please note setting progress = TRUE high values tasks leads less efficient parallelization. See details section additional information tasks integer 0 100 specifies frequently progress bar must updated. Default 0 simply split computation among workers. High values tasks can lead 15-30% slower p-value calculation. See details section additional information BPPARAM desired parallel computing behavior. parameter defaults BiocParallel::bpparam(), can edited. See BiocParallel::bpparam() information parallel computing R","code":""},{"path":"/reference/topologicalAnalysis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform a topologically-aware integrative pathway analysis (TAIPA) — topologicalAnalysis","text":"object class IntegrativePathwayAnalysis stores results analysis. See relative help page details.","code":""},{"path":[]},{"path":"/reference/topologicalAnalysis.html","id":"topologically-aware-integrative-pathway-analysis-taipa-","dir":"Reference","previous_headings":"","what":"Topologically-Aware Integrative Pathway Analysis (TAIPA)","title":"Perform a topologically-aware integrative pathway analysis (TAIPA) — topologicalAnalysis","text":"analysis aims identify biological pathways result affected miRNA mRNA dysregulations. analysis, biological pathways retrieved pathway database KEGG, interplay miRNAs genes added networks. network defined graph \\(G(V, E)\\), \\(V\\) represents nodes, \\(E\\) represents relationships nodes. , nodes significantly differentially expressed assigned weight \\(w_i = 1\\), whereas differentially expressed nodes assigned weight \\(w_i = \\left| \\Delta E_i \\right|\\), \\(\\Delta E_i\\) linear fold change node. Moreover, consider biological interaction two nodes, namely \\(\\) \\(j\\), define interaction parameter \\(\\beta_{\\rightarrow j} = 1\\) activation interactions \\(\\beta_{\\rightarrow j} = -1\\) repression interactions. Subsequently, concordance coefficient \\(\\gamma_{\\rightarrow j}\\) defined : $$\\gamma_{\\rightarrow j} = \\begin{cases} \\beta_{\\rightarrow j} &\\text{} sign(\\Delta E_i) = sign(\\Delta E_j) \\\\ - \\beta_{\\rightarrow j} &\\text{} sign(\\Delta E_i) \\= sign(\\Delta E_j) \\end{cases}\\,.$$ Later process, breadth-first search (BFS) algorithm applied topologically sort pathway nodes individual node occurs upstream nodes. Nodes within cycles considered leaf nodes. point, node score \\(\\phi\\) calculated pathway node \\(\\) : $$\\phi_i = w_i + \\sum_{j=1}^{U} \\gamma_{\\rightarrow j} \\cdot k_j\\,.$$ \\(U\\) represents number upstream nodes, \\(\\gamma_{\\rightarrow j}\\) denotes concordance coefficient, \\(k_j\\) propagation factor defined : $$k_j = \\begin{cases} w_j &\\text{} \\phi_j = 0 \\\\ \\phi_j &\\text{} \\phi_j \\= 0 \\end{cases}\\,.$$ Finally, pathway score \\(\\Psi\\) calculated : $$\\Psi = \\frac{1 - M}{N} \\cdot \\sum_{=1}^{N} \\phi_i\\,,$$ \\(M\\) represents proportion miRNAs pathway, \\(N\\) represents total number nodes network. , compute statistical significance pathway score, permutation procedure applied. Later, observed pathway scores permuted scores standardized subtracting mean score permuted sets \\(\\mu_{\\Psi_P}\\) dividing standard deviation permuted scores \\(\\sigma_{\\Psi_P}\\). Finally, p-value defined based fraction permutations reported higher normalized pathway score observed one. However, prevent p-values equal zero, define p-values : $$p = \\frac{\\sum_{n=1}^{N_p} \\left[ \\Psi_{P_N} \\ge \\Psi_N \\right] + 1} {N_p + 1}\\,.$$ end, p-values corrected multiple testing either max-T procedure (default option) particularly suited permutation tests, standard multiple testing approaches.","code":""},{"path":"/reference/topologicalAnalysis.html","id":"implementation-details","dir":"Reference","previous_headings":"","what":"Implementation details","title":"Perform a topologically-aware integrative pathway analysis (TAIPA) — topologicalAnalysis","text":"computational efficiency, pathway score computation implemented C++ language. Moreover, define statistical significance network, permutation test applied following number permutations specified nPerm. default setting perform 10000 permutations. higher number permutations, stable calculated p-values, even though time needed increase. regard, since computing pathway score 10000 networks pathway computationally intensive, parallel computing employed reduce running time. user can modify parallel computing behavior specifying BPPARAM parameter. See BiocParallel::bpparam() details. , progress bar can also included show completion percentage setting progress = TRUE. Moreover, user can define frequently progress bar gets updated tweaking tasks parameter. using progress = TRUE, setting tasks 100 tells function update progress bar 100 times, user can see increases 1%. Instead, setting tasks 50, means progress bar gets updated every 2% completion. However, keep mind tasks values 50 100 lead 15-30% slower p-value calculation due increased data transfer workers. Instead, lower tasks values like 20 determine less frequent progress updates slightly less efficient including progress bar.","code":""},{"path":"/reference/topologicalAnalysis.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Perform a topologically-aware integrative pathway analysis (TAIPA) — topologicalAnalysis","text":"Peter H. Westfall S. Stanley Young. Resampling-Based Multiple Testing: Examples Methods p-Value Adjustment. John Wiley & Sons. ISBN 978-0-471-55761-6.","code":""},{"path":"/reference/topologicalAnalysis.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Perform a topologically-aware integrative pathway analysis (TAIPA) — topologicalAnalysis","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/topologicalAnalysis.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform a topologically-aware integrative pathway analysis (TAIPA) — topologicalAnalysis","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # perform integration analysis with default settings obj <- mirnaIntegration(obj) #> Since data derive from paired samples, a correlation test will be used. #> Performing Spearman's correlation analysis... #> A statistically significant correlation between 215 miRNA-target pairs was found!  # \\donttest{ # retrieve pathways from KEGG and augment them with miRNA-gene interactions paths <- preparePathways(obj) #> Downloading pathways from KEGG database... #> Converting identifiers to gene symbols... #> Adding miRNA-gene interactions to biological pathways... #> Warning: 155 pathways have been ignored because they contain too few nodes with gene expression measurement. #> Performing topological sorting of pathway nodes...  # perform the integrative pathway analysis with 1000 permutations ipa <- topologicalAnalysis(obj, paths, nPerm = 1000) #> Calculating pathway scores... #> Generating random permutations... #> Calculating p-values with 1000 permutations... #> Correcting p-values through the max-T procedure... #> The topologically-aware integrative pathway analysis reported 1 significantly altered pathways!  # access the results of pathway analysis integratedPathways(ipa) #>                                             pathway  coverage    score #> Thyroid hormone synthesis Thyroid hormone synthesis 0.3469388 12.12941 #>                           normalized.score       P.Val adj.P.Val #> Thyroid hormone synthesis         7.657364 0.000999001     0.028  # create a dotplot of integrated pathways integrationDotplot(ipa)   # explore a specific biological network visualizeNetwork(ipa, \"Thyroid hormone synthesis\")  # }"},{"path":"/reference/visualizeNetwork.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize the relationships between miRNAs and genes in a biological pathway — visualizeNetwork","title":"Visualize the relationships between miRNAs and genes in a biological pathway — visualizeNetwork","text":"function can used plot augmented pathways created topologicalAnalysis() function. particular, given valid object class IntegrativePathwayAnalysis, function allows produce network graph specified biological pathway, alongside expression fold changes. way, augmented pathways made miRNAs genes can visually explored better investigate consequences miRNA/gene dysregulations.","code":""},{"path":"/reference/visualizeNetwork.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize the relationships between miRNAs and genes in a biological pathway — visualizeNetwork","text":"","code":"visualizeNetwork(   object,   pathway,   algorithm = \"dot\",   fontsize = 14,   lfcScale = c(\"royalblue\", \"white\", \"red\"),   nodeBorderCol = \"black\",   nodeTextCol = \"black\",   edgeCol = \"darkgrey\",   edgeWidth = 1,   subgraph = NULL,   highlightNodes = NULL,   highlightCol = \"gold\",   highlightWidth = 2,   legendColorbar = TRUE,   legendInteraction = TRUE,   title = NULL,   titleCex = 2,   titleFace = 1 )"},{"path":"/reference/visualizeNetwork.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize the relationships between miRNAs and genes in a biological pathway — visualizeNetwork","text":"object object class IntegrativePathwayAnalysis containing results miRNA-mRNA pathway analysis pathway name biological pathway show. available pathways given database can seen listPathways() function algorithm layout algorithm used arrange nodes network. must one dot (default), circo, fdp, neato, osage twopi. information regarding algorithms, please check details section fontsize font size node graph. Default 14 lfcScale must character vector length 3 containing valid R color names creating gradient log2 fold changes. first value refers downregulation, middle one stable expression, last one upregulation. Default value c('royalblue', 'white', 'red'). Available color formats include color names, 'blue' 'red', hexadecimal colors specified #RRGGBB nodeBorderCol must R color name specifies color node borders. Default black. Available color formats include color names, 'blue' 'red', hexadecimal colors specified #RRGGBB nodeTextCol must R color name specifies color miRNA/gene names. Default black. Available color formats include color names, 'blue' 'red', hexadecimal colors specified #RRGGBB edgeCol must R color name specifies color edges nodes. Default darkgrey. Available color formats include color names, 'blue' 'red', hexadecimal colors specified #RRGGBB edgeWidth width edges. Default 1 subgraph optional character vector containing nodes want maintain final plot. nodes shown. useful display specific features extremely messy graphs. Default NULL highlightNodes character vector containing names nodes want highlight. Default NULL highlight nodes. See details section additional information highlightCol must R color name specifies color edges borders highlighted nodes. Default gold. Available color formats include color names, 'blue' 'red', hexadecimal colors specified #RRGGBB highlightWidth width edges highlighted nodes. Default 2 legendColorbar Logical, whether add legend color bar log2 fold changes. Default TRUE legendInteraction Logical, whether add legend links edge types biological interactions. Default TRUE title title plot. Default NULL include plot title titleCex cex plot main title. Default 2 titleFace integer specifies font use title. 1 corresponds plain text, 2 bold face, 3 italic, 4 bold italic, 5 symbol font. Default 1","code":""},{"path":"/reference/visualizeNetwork.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Visualize the relationships between miRNAs and genes in a biological pathway — visualizeNetwork","text":"base R plot augmented pathway.","code":""},{"path":"/reference/visualizeNetwork.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Visualize the relationships between miRNAs and genes in a biological pathway — visualizeNetwork","text":"network created function highly flexible, allowing tweak different parameters can influence resulting graph, including node highlighting, layout algorithms, colors, legends.","code":""},{"path":"/reference/visualizeNetwork.html","id":"nodes-included-in-the-plot","dir":"Reference","previous_headings":"","what":"Nodes included in the plot","title":"Visualize the relationships between miRNAs and genes in a biological pathway — visualizeNetwork","text":"huge messy networks, user can specify nodes include plot subgraph parameter, order represent features wants display. Alternatively, parameter can set NULL (default), plot nodes biological pathway.","code":""},{"path":"/reference/visualizeNetwork.html","id":"highlight-nodes-and-edges","dir":"Reference","previous_headings":"","what":"Highlight nodes and edges","title":"Visualize the relationships between miRNAs and genes in a biological pathway — visualizeNetwork","text":"One interesting feature offered function consists highlighting specific nodes edges within network. results particularly useful want put evidence affected routes biological pathway. highlight nodes, must provide highlightNodes parameter character vector lists desired nodes. result, borders highlighted nodes colored according highlightCol parameter (default 'gold'), width specified highlightWidth (default 2). Notably, function automatically highlights way edges connecting selected nodes.","code":""},{"path":"/reference/visualizeNetwork.html","id":"layout-algorithms","dir":"Reference","previous_headings":"","what":"Layout algorithms","title":"Visualize the relationships between miRNAs and genes in a biological pathway — visualizeNetwork","text":"Furthermore, function allows use different methods lay nodes network setting algorithm parameter. regard, several algorithms Rgraphviz package can used, namely: dot (default), algorithm attributed Sugiyama et al. described Gansner et al., creates ranked layout particularly suited display hierarchies complex pathways; circo, uses recursive radial algorithm resulting circular layout; fdp, adopts force-directed approach similar Fruchterman Reingold; neato, relies spring model iterative solver finds low energy configurations; osage, layout engine recursively draws cluster subgraphs; twopi, places node center network, arranges remaining nodes series concentric circles around center. additional information algorithms, refer Rgraphviz::GraphvizLayouts.","code":""},{"path":"/reference/visualizeNetwork.html","id":"customization","dir":"Reference","previous_headings":"","what":"Customization","title":"Visualize the relationships between miRNAs and genes in a biological pathway — visualizeNetwork","text":"customize look resulting plot, function allows change different graphical parameters, including: color scale log2 fold changes, can set lfcScale; font size nodes, can changed fontsize; border color nodes, can edited nodeBorderCol; text color nodes, can changed nodeTextCol; color used edges, set edgeCol; width edges, customizable edgeWidth. Additionally, function allows include handy legends useful interpreting biological consequences network alterations. particular: color bar legend displaying log2 fold changes corresponding fill color can included legendColorbar = TRUE (default); legend links appearance edges arrow heads type biological interaction can shown legendInteraction = TRUE (default). Lastly, title, titleCex titleFace parameters can tweaked include network title desired look.","code":""},{"path":"/reference/visualizeNetwork.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Visualize the relationships between miRNAs and genes in a biological pathway — visualizeNetwork","text":"function uses Rgraphviz package render network object.","code":""},{"path":"/reference/visualizeNetwork.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Visualize the relationships between miRNAs and genes in a biological pathway — visualizeNetwork","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/visualizeNetwork.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualize the relationships between miRNAs and genes in a biological pathway — visualizeNetwork","text":"","code":"# load example IntegrativePathwayAnalysis object obj <- loadExamples(\"IntegrativePathwayAnalysis\")  # \\donttest{ # explore a specific biological network visualizeNetwork(obj, \"Thyroid hormone synthesis\")  # }"},{"path":"/news/index.html","id":"mirit-0990","dir":"Changelog","previous_headings":"","what":"MIRit 0.99.0","title":"MIRit 0.99.0","text":"Initial version Bioconductor submission.","code":""}]
+[{"path":"/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to MIRit","title":"Contributing to MIRit","text":"outlines propose change MIRit. detailed discussion contributing tidyverse packages, please see development contributing guide code review principles.","code":""},{"path":"/CONTRIBUTING.html","id":"fixing-typos","dir":"","previous_headings":"","what":"Fixing typos","title":"Contributing to MIRit","text":"can fix typos, spelling mistakes, grammatical errors documentation directly using GitHub web interface, long changes made source file. generally means ’ll need edit roxygen2 comments .R, .Rd file. can find .R file generates .Rd reading comment first line.","code":""},{"path":"/CONTRIBUTING.html","id":"bigger-changes","dir":"","previous_headings":"","what":"Bigger changes","title":"Contributing to MIRit","text":"want make bigger change, ’s good idea first file issue make sure someone team agrees ’s needed. ’ve found bug, please file issue illustrates bug minimal reprex (also help write unit test, needed). See guide create great issue advice.","code":""},{"path":"/CONTRIBUTING.html","id":"pull-request-process","dir":"","previous_headings":"Bigger changes","what":"Pull request process","title":"Contributing to MIRit","text":"Fork package clone onto computer. haven’t done , recommend using usethis::create_from_github(\"jacopo-ronchi/MIRit\", fork = TRUE). Install development dependencies devtools::install_dev_deps(), make sure package passes R CMD check running devtools::check(). R CMD check doesn’t pass cleanly, ’s good idea ask help continuing. Create Git branch pull request (PR). recommend using usethis::pr_init(\"brief-description--change\"). Make changes, commit git, create PR running usethis::pr_push(), following prompts browser. title PR briefly describe change. body PR contain Fixes #issue-number. user-facing changes, add bullet top NEWS.md (.e. just first header). Follow style described https://style.tidyverse.org/news.html.","code":""},{"path":"/CONTRIBUTING.html","id":"code-style","dir":"","previous_headings":"Bigger changes","what":"Code style","title":"Contributing to MIRit","text":"New code follow tidyverse style guide. can use styler package apply styles, please don’t restyle code nothing PR. use roxygen2, Markdown syntax, documentation. use testthat unit tests. Contributions test cases included easier accept.","code":""},{"path":"/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contributing to MIRit","text":"Please note MIRit project released Contributor Code Conduct. contributing project agree abide terms.","code":""},{"path":"/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"GNU General Public License","title":"GNU General Public License","text":"Version 3, 29 June 2007Copyright © 2007 Free Software Foundation, Inc. <http://fsf.org/> Everyone permitted copy distribute verbatim copies license document, changing allowed.","code":""},{"path":"/LICENSE.html","id":"preamble","dir":"","previous_headings":"","what":"Preamble","title":"GNU General Public License","text":"GNU General Public License free, copyleft license software kinds works. licenses software practical works designed take away freedom share change works. contrast, GNU General Public License intended guarantee freedom share change versions program–make sure remains free software users. , Free Software Foundation, use GNU General Public License software; applies also work released way authors. can apply programs, . speak free software, referring freedom, price. 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Limitation of Liability","title":"GNU General Public License","text":"EVENT UNLESS REQUIRED APPLICABLE LAW AGREED WRITING COPYRIGHT HOLDER, PARTY MODIFIES /CONVEYS PROGRAM PERMITTED , LIABLE DAMAGES, INCLUDING GENERAL, SPECIAL, INCIDENTAL CONSEQUENTIAL DAMAGES ARISING USE INABILITY USE PROGRAM (INCLUDING LIMITED LOSS DATA DATA RENDERED INACCURATE LOSSES SUSTAINED THIRD PARTIES FAILURE PROGRAM OPERATE PROGRAMS), EVEN HOLDER PARTY ADVISED POSSIBILITY DAMAGES.","code":""},{"path":"/LICENSE.html","id":"id_17-interpretation-of-sections-15-and-16","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"17. Interpretation of Sections 15 and 16","title":"GNU General Public License","text":"disclaimer warranty limitation liability provided given local legal effect according terms, reviewing courts shall apply local law closely approximates absolute waiver civil liability connection Program, unless warranty assumption liability accompanies copy Program return fee. END TERMS CONDITIONS","code":""},{"path":"/LICENSE.html","id":"how-to-apply-these-terms-to-your-new-programs","dir":"","previous_headings":"","what":"How to Apply These Terms to Your New Programs","title":"GNU General Public License","text":"develop new program, want greatest possible use public, best way achieve make free software everyone can redistribute change terms. , attach following notices program. safest attach start source file effectively state exclusion warranty; file least “copyright” line pointer full notice found. Also add information contact electronic paper mail. program terminal interaction, make output short notice like starts interactive mode: hypothetical commands show w show c show appropriate parts General Public License. course, program’s commands might different; GUI interface, use “box”. also get employer (work programmer) school, , sign “copyright disclaimer” program, necessary. information , apply follow GNU GPL, see <http://www.gnu.org/licenses/>. GNU General Public License permit incorporating program proprietary programs. program subroutine library, may consider useful permit linking proprietary applications library. want , use GNU Lesser General Public License instead License. first, please read <http://www.gnu.org/philosophy/--lgpl.html>.","code":"<one line to give the program's name and a brief idea of what it does.> Copyright (C) <year>  <name of author>  This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.  This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more details.  You should have received a copy of the GNU General Public License along with this program.  If not, see <http://www.gnu.org/licenses/>. <program>  Copyright (C) <year>  <name of author> This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'. This is free software, and you are welcome to redistribute it under certain conditions; type 'show c' for details."},{"path":"/SUPPORT.html","id":null,"dir":"","previous_headings":"","what":"Getting help with MIRit","title":"Getting help with MIRit","text":"Thank using MIRit! filing issue, things know make process smooth possible parties.","code":""},{"path":"/SUPPORT.html","id":"make-a-reprex","dir":"","previous_headings":"","what":"Make a reprex","title":"Getting help with MIRit","text":"Start making minimally reproducible example, also known ‘reprex’. may use reprex R package create one, though necessary help. make R-question-asking endeavors easier. Learning use takes 5 10 minutes. tips make minimally reproducible example, see StackOverflow link.","code":""},{"path":"/SUPPORT.html","id":"where-to-post-it","dir":"","previous_headings":"","what":"Where to post it?","title":"Getting help with MIRit","text":"Bioconductor help web page gives overview places may help answer question. Bioconductor software related questions, bug reports feature requests, addressed appropriate Bioconductor/MIRit GitHub repository. Follow bug report feature request templates GitHub. package GitHub repository, see next bullet point. Bioconductor software usage questions addressed Bioconductor Support Website. Make sure use appropriate package tag, otherwise package authors get notification. General R questions can posed StackOverflow RStudio Community website especially pertain tidyverse RStudio GUI related products.","code":""},{"path":"/SUPPORT.html","id":"issues-or-feature-requests","dir":"","previous_headings":"","what":"Issues or Feature Requests","title":"Getting help with MIRit","text":"opening new issue feature request, sure search issues pull requests ensure one already exist implemented development version. Note. can remove :open search term issues page search open closed issues. See link learn modifying search.","code":""},{"path":"/SUPPORT.html","id":"what-happens-next","dir":"","previous_headings":"","what":"What happens next?","title":"Getting help with MIRit","text":"Bioconductor maintainers limited resources strive responsive possible. Please forget tag appropriate maintainer issue GitHub username (e.g., @username). order make easy possible Bioconductor core developers remediate issue. Provide accurate, brief, reproducible report outlined issue templates. Thank trusting Bioconductor.","code":""},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Jacopo Ronchi. Author, maintainer. Maria Foti. Funder.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Ronchi J Foti M. 'MIRit: integrative R framework identification impaired miRNA-mRNA regulatory networks complex diseases'. bioRxiv (2023). doi:10.1101/2023.11.24.568528","code":"@Article{,   title = {MIRit: an integrative R framework for the identification of impaired miRNA-mRNA regulatory networks in complex diseases},   author = {Jacopo Ronchi and Maria Foti},   year = {2023},   journal = {bioRxiv},   doi = {10.1101/2023.11.24.568528},   url = {https://doi.org/10.1101/2023.11.24.568528}, }"},{"path":"/index.html","id":"mirit-","dir":"","previous_headings":"","what":"Integrative miRNA-mRNA analysis with MIRit","title":"Integrative miRNA-mRNA analysis with MIRit","text":"MIRit (miRNA integration tool) open-source R package aims facilitate comprehension microRNA (miRNA) biology integrative analysis gene miRNA expression data deriving different platforms, including microarrays, RNA-Seq, miRNA-Seq, proteomics single-cell transcriptomics. Given regulatory importance, complete characterization miRNA dysregulations results crucial explore molecular networks may lead insurgence complex diseases. purpose, developed MIRit, --one framework provides flexible powerful methods performing integrative miRNA-mRNA multi-omic analyses start finish.","code":""},{"path":"/index.html","id":"authors","dir":"","previous_headings":"","what":"Authors","title":"Integrative miRNA-mRNA analysis with MIRit","text":"Dr. Jacopo Ronchi 1 (author maintainer) Dr. Maria Foti 1 1School Medicine Surgery, University Milano-Bicocca, Italy","code":""},{"path":"/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Integrative miRNA-mRNA analysis with MIRit","text":"Get latest stable R release CRAN. install MIRit Bioconductor using following code: Alternatively, development version MIRit can installed GitHub :","code":"if (!requireNamespace(\"BiocManager\", quietly = TRUE)) {     install.packages(\"BiocManager\") }  BiocManager::install(\"MIRit\") BiocManager::install(\"jacopo-ronchi/MIRit\")"},{"path":"/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Integrative miRNA-mRNA analysis with MIRit","text":"detailed instructions use MIRit integrative miRNA-mRNA analysis, please refer package vignette Bioconductor. Alternatively, can refer documentation website.","code":""},{"path":"/index.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Integrative miRNA-mRNA analysis with MIRit","text":"use MIRit published research, please cite corresponding paper: Ronchi J Foti M. ‘MIRit: integrative R framework identification impaired miRNA-mRNA regulatory networks complex diseases’. bioRxiv (2023). doi:10.1101/2023.11.24.568528 Please note MIRit package made possible thanks many R bioinformatics software authors, cited either vignettes /paper(s) describing package.","code":""},{"path":"/index.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Integrative miRNA-mRNA analysis with MIRit","text":"Please note MIRit project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":"/index.html","id":"development-tools","dir":"","previous_headings":"","what":"Development tools","title":"Integrative miRNA-mRNA analysis with MIRit","text":"Continuous code testing possible thanks GitHub actions usethis, remotes, rcmdcheck customized use Bioconductor’s docker containers BiocCheck. Code coverage assessment possible thanks codecov covr. documentation website automatically updated thanks pkgdown. code styled automatically thanks styler. documentation formatted thanks devtools roxygen2. details, check dev directory. package developed using biocthis.","code":""},{"path":"/reference/FunctionalEnrichment-class.html","id":null,"dir":"Reference","previous_headings":"","what":"The FunctionalEnrichment class — FunctionalEnrichment-class","title":"The FunctionalEnrichment class — FunctionalEnrichment-class","text":"class introduces possibility store results functional enrichment analyses -representation analysis (ORA), gene set enrichment analysis (GSEA), competitive gene set test accounting inter-gene correlation (CAMERA). different slots contained class used store enrichment results generated enrichGenes() function.","code":""},{"path":"/reference/FunctionalEnrichment-class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The FunctionalEnrichment class — FunctionalEnrichment-class","text":"","code":"# S4 method for FunctionalEnrichment enrichmentResults(object)  # S4 method for FunctionalEnrichment enrichmentDatabase(object)  # S4 method for FunctionalEnrichment enrichmentMethod(object)  # S4 method for FunctionalEnrichment geneSet(object)  # S4 method for FunctionalEnrichment enrichmentMetric(object)  # S4 method for FunctionalEnrichment enrichedFeatures(object)  # S4 method for FunctionalEnrichment show(object)"},{"path":"/reference/FunctionalEnrichment-class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The FunctionalEnrichment class — FunctionalEnrichment-class","text":"object object class FunctionalEnrichment containing enrichment results","code":""},{"path":"/reference/FunctionalEnrichment-class.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"The FunctionalEnrichment class — FunctionalEnrichment-class","text":"enrichmentResults(FunctionalEnrichment): Access data slot take closer look enriched terms enrichment analysis enrichmentDatabase(FunctionalEnrichment): See database used functional enrichment enrichmentMethod(FunctionalEnrichment): Visualize approach used functional enrichment analysis geneSet(FunctionalEnrichment): Access geneSet slot see collection gene sets used GSEA enrichmentMetric(FunctionalEnrichment): View ranking metric used GSEA enrichedFeatures(FunctionalEnrichment): View names pre-ranked features used GSEA show(FunctionalEnrichment): Show method objects class FunctionalEnrichment","code":""},{"path":"/reference/FunctionalEnrichment-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"The FunctionalEnrichment class — FunctionalEnrichment-class","text":"data data.frame object holding output enrichment analysis method method used perform functional enrichment analysis (e.g. Gene Set Enrichment Analysis (GSEA)) organism name organism consideration (e.g. Homo sapiens) database name database used enrichment analysis (e.g. KEGG) pCutoff numeric value defining threshold used statistical significance enrichment analysis (e.g. 0.05) pAdjustment character indicating method used correct p-values multiple testing (e.g. fdr) features character vector containing list features used enrichment statistic numeric vector containing statistic used run GSEA. parameter empty ORA CAMERA universe background universe features. Typically, equal complete list features assayed. slot NULL GSEA geneSet gene set used functional enrichment analysis. list object element contains list genes belonging specific pathway.","code":""},{"path":"/reference/FunctionalEnrichment-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The FunctionalEnrichment class — FunctionalEnrichment-class","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/IntegrativePathwayAnalysis-class.html","id":null,"dir":"Reference","previous_headings":"","what":"The IntegrativePathwayAnalysis class — IntegrativePathwayAnalysis-class","title":"The IntegrativePathwayAnalysis class — IntegrativePathwayAnalysis-class","text":"class stores output integrative multi-omic pathway analyses. particular, slots class suitable represent results topologically-aware integrative pathway analysis (TAIPA) returned topologicalAnalysis() function.","code":""},{"path":"/reference/IntegrativePathwayAnalysis-class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The IntegrativePathwayAnalysis class — IntegrativePathwayAnalysis-class","text":"","code":"# S4 method for IntegrativePathwayAnalysis integratedPathways(object)  # S4 method for IntegrativePathwayAnalysis integrationDatabase(object)  # S4 method for IntegrativePathwayAnalysis augmentedPathways(object)  # S4 method for IntegrativePathwayAnalysis show(object)"},{"path":"/reference/IntegrativePathwayAnalysis-class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The IntegrativePathwayAnalysis class — IntegrativePathwayAnalysis-class","text":"object object class IntegrativePathwayAnalysis containing results miRNA-mRNA pathway analysis","code":""},{"path":[]},{"path":"/reference/IntegrativePathwayAnalysis-class.html","id":"analysis-results","dir":"Reference","previous_headings":"","what":"Analysis results","title":"The IntegrativePathwayAnalysis class — IntegrativePathwayAnalysis-class","text":"data slot class consists data.frame object six columns, namely: pathway, indicates name biological network; coverage, specifies fraction nodes expression measurement available; score, expresses score individual pathway; normalized.score, indicates pathway scores standardizing values null distribution computed permutations; P.Val, resulting p-value pathway; adj.P.Val, p-value adjusted multiple testing.","code":""},{"path":"/reference/IntegrativePathwayAnalysis-class.html","id":"organisms-and-databases","dir":"Reference","previous_headings":"","what":"Organisms and databases","title":"The IntegrativePathwayAnalysis class — IntegrativePathwayAnalysis-class","text":"organism database slots specify organism study database used retrieving biological interactions, respectively. particular, topologicalAnalysis() function supports KEGG, WikiPathways, Reactome databases. Regarding organisms, supportedOrganisms() function can used retrieve available species database.","code":""},{"path":"/reference/IntegrativePathwayAnalysis-class.html","id":"statistical-significance-of-the-permutation-test","dir":"Reference","previous_headings":"","what":"Statistical significance of the permutation test","title":"The IntegrativePathwayAnalysis class — IntegrativePathwayAnalysis-class","text":"pCutoff pAdjustment slots refer cutoff used analysis. pCutoff threshold used defining statistically significant pathways, whereas pAdjustment refers multiple testing correction method used. Furthermore, since statistical significance pathway defined basis permutation test, number permutations also specified nPerm slot.","code":""},{"path":"/reference/IntegrativePathwayAnalysis-class.html","id":"augmented-pathways","dir":"Reference","previous_headings":"","what":"Augmented pathways","title":"The IntegrativePathwayAnalysis class — IntegrativePathwayAnalysis-class","text":"pathways slot contains list weighted graph objects, representing biological pathway. networks enlarged adding observed miRNA-mRNA interactions. network processed weight edge +1 activation interactions, -1 repression interactions, occurring miRNAs mRNAs.","code":""},{"path":"/reference/IntegrativePathwayAnalysis-class.html","id":"differential-expression-results-for-both-mirnas-and-genes","dir":"Reference","previous_headings":"","what":"Differential expression results for both miRNAs and genes","title":"The IntegrativePathwayAnalysis class — IntegrativePathwayAnalysis-class","text":"expression variation miRNAs genes measured study stored expression slot. particular, slot consists data.frame object different information, including log2 fold changes, node weights p-values.","code":""},{"path":"/reference/IntegrativePathwayAnalysis-class.html","id":"minimum-percentage-of-measured-features","dir":"Reference","previous_headings":"","what":"Minimum percentage of measured features","title":"The IntegrativePathwayAnalysis class — IntegrativePathwayAnalysis-class","text":"minPc slot indicates minimum percentage miRNAs/mRNAs pathways considered integrative analysis. needed often, differential expression analysis performed, lowly expressed features removed. Therefore, pathways might result significantly affected even 1% nodes perturbed.","code":""},{"path":"/reference/IntegrativePathwayAnalysis-class.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"The IntegrativePathwayAnalysis class — IntegrativePathwayAnalysis-class","text":"integratedPathways(IntegrativePathwayAnalysis): Access results integrative miRNA-mRNA pathway analysis integrationDatabase(IntegrativePathwayAnalysis): View database used integrative pathway analysis augmentedPathways(IntegrativePathwayAnalysis): Extract list biological networks augmented miRNA-mRNA interactions show(IntegrativePathwayAnalysis): Show method objects class IntegrativePathwayAnalysis","code":""},{"path":"/reference/IntegrativePathwayAnalysis-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"The IntegrativePathwayAnalysis class — IntegrativePathwayAnalysis-class","text":"data data.frame object contains results integrative pathway analysis. See details section details method method used analysis organism name organism consideration (e.g. Homo sapiens) database name database used retrieving biological pathways (e.g. KEGG) pCutoff numeric value defining threshold used statistical significance (e.g. 0.05) pAdjustment character indicating method used correct p-values multiple testing (e.g. fdr) pathways list graph objects containing biological networks retrieved database, augmented miRNA-mRNA interactions expression data.frame object containing differential expression results miRNAs genes minPc minimum percentage measured features pathway must considered analysis nPerm number permutation used assessing statistical significance pathway","code":""},{"path":"/reference/IntegrativePathwayAnalysis-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The IntegrativePathwayAnalysis class — IntegrativePathwayAnalysis-class","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/MIRit-package.html","id":null,"dir":"Reference","previous_headings":"","what":"MIRit: Integrate microRNA and gene expression to decipher pathway complexity — MIRit-package","title":"MIRit: Integrate microRNA and gene expression to decipher pathway complexity — MIRit-package","text":"MIRit R package provides several methods investigating relationships miRNAs genes different biological conditions. particular, MIRit allows explore functions dysregulated miRNAs, makes possible identify miRNA-gene regulatory axes control biological pathways, thus enabling users unveil complexity miRNA biology. MIRit --one framework aims help researchers central aspects integrative miRNA-mRNA analyses, differential expression analysis network characterization.","code":""},{"path":[]},{"path":"/reference/MIRit-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"MIRit: Integrate microRNA and gene expression to decipher pathway complexity — MIRit-package","text":"Maintainer: Jacopo Ronchi jacopo.ronchi@unimib.(ORCID)","code":""},{"path":"/reference/MirnaExperiment-class.html","id":null,"dir":"Reference","previous_headings":"","what":"The 'MirnaExperiment' class — MirnaExperiment-class","title":"The 'MirnaExperiment' class — MirnaExperiment-class","text":"class extends MultiAssayExperiment homonym package provide flexibility handling genomic data microRNAs targets, allowing store information microRNA gene expression, differential expression results, microRNA targets miRNA-gene integration analysis. class can used manage genomic data deriving different sources, like RNA-Seq, microarrays mass spectrometry. Moreover, microRNA gene expression levels may originate individuals (paired samples) different subjects (unpaired samples).","code":""},{"path":"/reference/MirnaExperiment-class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The 'MirnaExperiment' class — MirnaExperiment-class","text":"","code":"# S4 method for MirnaExperiment mirnaDE(object, onlySignificant = TRUE, param = FALSE, returnObject = FALSE)  # S4 method for MirnaExperiment geneDE(object, onlySignificant = TRUE, param = FALSE, returnObject = FALSE)  # S4 method for MirnaExperiment significantMirnas(object)  # S4 method for MirnaExperiment significantGenes(object)  # S4 method for MirnaExperiment pairedSamples(object)  # S4 method for MirnaExperiment mirnaTargets(object)  # S4 method for MirnaExperiment integration(object, param = FALSE)  # S4 method for MirnaExperiment show(object)"},{"path":"/reference/MirnaExperiment-class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The 'MirnaExperiment' class — MirnaExperiment-class","text":"object object class MirnaExperiment onlySignificant Logical, TRUE differential expression results returned just statistically significant miRNAs/genes, FALSE full table miRNA/gene differential expression provided. Default TRUE report significant miRNAs/genes param Logical, whether return complete list object parameters used, just results stored data. Default FALSE returnObject Logical, TRUE function return limma/edgeR/DESeq2 object used differential expression analysis","code":""},{"path":"/reference/MirnaExperiment-class.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"The 'MirnaExperiment' class — MirnaExperiment-class","text":"mirnaDE(MirnaExperiment): Access results miRNA differential expression geneDE(MirnaExperiment): Access results gene differential expression significantMirnas(MirnaExperiment): Access names differentially expressed miRNAs significantGenes(MirnaExperiment): Access names differentially expressed genes pairedSamples(MirnaExperiment): Check object derives sample-matched data mirnaTargets(MirnaExperiment): Extract miRNA-targets interactions retrieved differentially expressed miRNAs integration(MirnaExperiment): Access results integrative miRNA-mRNA analysis show(MirnaExperiment): Show method objects class MirnaExperiment","code":""},{"path":"/reference/MirnaExperiment-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"The 'MirnaExperiment' class — MirnaExperiment-class","text":"ExperimentList ExperimentList class object assay dataset colData DataFrame clinical/specimen data available across experiments sampleMap DataFrame translatable identifiers samples participants metadata Additional data describing object drops metadata list dropped information mirnaDE list object containing results miRNA differential expression geneDE list object containing results gene differential expression pairedSamples logical parameter specifies whether miRNA gene expression measurements derive individuals (TRUE) different subjects (FALSE) targets data.frame object containing miRNA-target pairs. slot commonly populated getTargets() function integration list object containing results integration analysis miRNA gene expression values. slot commonly populated mirnaIntegration() function","code":""},{"path":"/reference/MirnaExperiment-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"The 'MirnaExperiment' class — MirnaExperiment-class","text":"create MirnaExperiment object, can use MirnaExperiment() constructor function, allows easily build verify valid object starting miRNA gene expression matrices.","code":""},{"path":"/reference/MirnaExperiment-class.html","id":"experimentlist","dir":"Reference","previous_headings":"","what":"ExperimentList","title":"The 'MirnaExperiment' class — MirnaExperiment-class","text":"ExperimentList slot designed contain results experiment/assay. case, holds miRNA gene expression matrices. contains SimpleList-class.","code":""},{"path":"/reference/MirnaExperiment-class.html","id":"coldata","dir":"Reference","previous_headings":"","what":"colData","title":"The 'MirnaExperiment' class — MirnaExperiment-class","text":"colData slot collection primary specimen data valid across experiments. slot strictly class DataFrame arguments constructor function allow arguments class data.frame subsequently coerced.","code":""},{"path":"/reference/MirnaExperiment-class.html","id":"samplemap","dir":"Reference","previous_headings":"","what":"sampleMap","title":"The 'MirnaExperiment' class — MirnaExperiment-class","text":"sampleMap contains DataFrame translatable identifiers samples participants biological units. standard column names sampleMap \"assay\", \"primary\", \"colname\". Note \"assay\" column factor corresponding names experiment ExperimentList. case names match sampleMap experiments, documented experiments sampleMap take precedence experiments dropped harmonization procedure. constructor function generate sampleMap case provided method may 'safer' alternative creating MultiAssayExperiment (long rownames identical colData, provided). empty sampleMap may produce empty experiments levels \"assay\" factor sampleMap match names ExperimentList.","code":""},{"path":"/reference/MirnaExperiment-class.html","id":"mirnade-and-genede","dir":"Reference","previous_headings":"","what":"mirnaDE and geneDE","title":"The 'MirnaExperiment' class — MirnaExperiment-class","text":"mirnaDE geneDE consist two list objects storing information regarding miRNA gene differential expression, including: data, contains differential expression results data.frame five columns: ID: indicates name miRNA/gene; logFC: indicates fold change feature logarithmic scale; AveExpr: represents average expression miRNA/gene; P.Value: indicates resulting p-value; adj.P.Val: contains p-values adjusted multiple testing. significant, character vector containing names significantly differentially expressed miRNAs/genes passed thresholds; method, specifies procedure used determine differentially expressed miRNAs/gens (eg. \"limma-voom\", \"edgeR\", \"DESeq2\", \"limma\"); group, column name variable (colData) used differential expression analysis; contrast, represents groups compared differential expression analysis (e.g. 'disease-healthy'); design, outlines R formula used fitting model. includes variable interest (group) together eventual covariates (e.g. '~ 0 + disease + sex'); pCutoff, indicates p-value cutoff used DE analysis; pAdjustment, approach used multiple testing correction; logFC, states log2 Fold Change cutoff used DE analysis; deObject, object deriving limma/edgeR/DESeq2, holds additional information regarding data processing. MiRNA differential expression results can accessed mirnaDE() function, additional details see ?mirnaDE. Similarly, gene differential expression results can accessed geneDE() function, additional details see ?geneDE.","code":""},{"path":"/reference/MirnaExperiment-class.html","id":"pairedsamples","dir":"Reference","previous_headings":"","what":"pairedSamples","title":"The 'MirnaExperiment' class — MirnaExperiment-class","text":"already mentioned, pairedSamples must TRUE miRNA gene expression derive subjects, must FALSE case.","code":""},{"path":"/reference/MirnaExperiment-class.html","id":"targets","dir":"Reference","previous_headings":"","what":"targets","title":"The 'MirnaExperiment' class — MirnaExperiment-class","text":"targets data.frame miRNA-target interactions, retrieved getTargets() function.","code":""},{"path":"/reference/MirnaExperiment-class.html","id":"integration","dir":"Reference","previous_headings":"","what":"integration","title":"The 'MirnaExperiment' class — MirnaExperiment-class","text":"Lastly, integration slot contains list object stores results options used performing integrative miRNA-gene analysis. particular, integration contains: data, data.frame object results integrative analysis; method, specifies procedure used perform integrative analysis; pCutoff, indicates p-value cutoff used analysis; pAdjustment, approach used multiple testing correction. Moreover, data differs basis integration strategy used. one-sided association test integration, integration based rotation gene set tests, data.frame seven columns: microRNA: miRNA ID; mirna.direction: fold change direction DE-miRNA (); gene.direction: fold change direction target genes (); DE: represents number differentially expressed targets; targets: represents total number targets miRNA; P.Val: indicates resulting p-value; adj.P.Val: contains test p-values corrected multiple testing; DE.targets: contains list differentially expressed targets whose expression negatively associated miRNA expression. Instead, correlation analysis performed, data six columns: microRNA: miRNA ID; Target: correlated target gene; microRNA.Direction: fold change direction DE-miRNA; Corr.Coeff: value correlation coefficient used; Corr.P.Value: p-value resulting correlation analysis; Corr.Adjusted.P.Val: contains correlation p-values corrected multiple testing. access results integrative analysis, data slot can accessed integration() function.","code":""},{"path":"/reference/MirnaExperiment-class.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"The 'MirnaExperiment' class — MirnaExperiment-class","text":"Marcel Ramos et al. Software Integration Multiomics Experiments Bioconductor. Cancer Research, 2017 November 1; 77(21); e39-42. DOI: 10.1158/0008-5472.CAN-17-0344","code":""},{"path":[]},{"path":"/reference/MirnaExperiment-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The 'MirnaExperiment' class — MirnaExperiment-class","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/MirnaExperiment.html","id":null,"dir":"Reference","previous_headings":"","what":"The constructor function for MirnaExperiment — MirnaExperiment","title":"The constructor function for MirnaExperiment — MirnaExperiment","text":"constructor function allows easily create objects class MirnaExperiment. function requires inputs miRNA gene expression matrices, well sample metadata.","code":""},{"path":"/reference/MirnaExperiment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The constructor function for MirnaExperiment — MirnaExperiment","text":"","code":"MirnaExperiment(mirnaExpr, geneExpr, samplesMetadata, pairedSamples = TRUE)"},{"path":"/reference/MirnaExperiment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The constructor function for MirnaExperiment — MirnaExperiment","text":"mirnaExpr matrix object containing microRNA expression levels. objects coercible matrix also accepted (e.g. data.frame). object must structured specified details section geneExpr matrix object containing gene expression levels. objects coercible matrix also accepted (e.g. data.frame). object must structured specified details section samplesMetadata data.frame object containing information samples used microRNA gene expression profiling. information see details section pairedSamples Logical, whether miRNA gene expression levels derive subjects . Check details section additional instructions. Default TRUE","code":""},{"path":"/reference/MirnaExperiment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The constructor function for MirnaExperiment — MirnaExperiment","text":"valid MirnaExperiment object containing information miRNA gene expression.","code":""},{"path":"/reference/MirnaExperiment.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"The constructor function for MirnaExperiment — MirnaExperiment","text":"function requires data prepared described .","code":""},{"path":"/reference/MirnaExperiment.html","id":"mirnaexpr-and-geneexpr","dir":"Reference","previous_headings":"","what":"mirnaExpr and geneExpr","title":"The constructor function for MirnaExperiment — MirnaExperiment","text":"mirnaExpr geneExpr must matrix objects (objects coercible one) contain miRNA gene expression values, respectively. Rows must represent different miRNAs/genes analyzed columns must represent different samples study. mirnaExpr, row names must contain miRNA names according miRBase nomenclature, whereas geneExpr, row names must contain gene symbols according hgnc nomenclature. values contained objects can derive microarray RNA-Seq experiments. NGS experiments, mirnaExpr geneExpr just un-normalized count matrices. Instead, microarray experiments, data normalized log2 transformed, example RMA algorithm.","code":""},{"path":"/reference/MirnaExperiment.html","id":"samplesmetadata","dir":"Reference","previous_headings":"","what":"samplesMetadata","title":"The constructor function for MirnaExperiment — MirnaExperiment","text":"samplesMetadata must data.frame object containing information samples used miRNA profiling gene expression analysis. Specifically, data.frame must contain: column named primary, specifying identifier sample; column named mirnaCol, containing column names used sample mirnaExpr object; column named geneCol, containing column names used sample geneExpr object; eventual columns define specific sample metadata, disease condition, age, sex ... unpaired samples, NAs can used missing entries mirnaCol/geneCol.","code":""},{"path":"/reference/MirnaExperiment.html","id":"pairedsamples","dir":"Reference","previous_headings":"","what":"pairedSamples","title":"The constructor function for MirnaExperiment — MirnaExperiment","text":"MicroRNA gene expression measurements may derive subjects (.e. samples used generate miRNA gene expression data) different individuals (.e. miRNA expression assayed group samples gene expression retrieved different group samples). pairedSamples logical parameter defines relationship miRNA gene expression measurements. must TRUE data derive individuals, must FALSE data derive different subjects.","code":""},{"path":"/reference/MirnaExperiment.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The constructor function for MirnaExperiment — MirnaExperiment","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/MirnaExperiment.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The constructor function for MirnaExperiment — MirnaExperiment","text":"","code":"# load example data data(geneCounts, package = \"MIRit\") data(mirnaCounts, package = \"MIRit\")  # create samples metadata meta <- data.frame(     \"primary\" = colnames(geneCounts),     \"mirnaCol\" = colnames(mirnaCounts), \"geneCol\" = colnames(geneCounts),     \"disease\" = c(rep(\"PTC\", 8), rep(\"NTH\", 8)),     \"patient\" = c(rep(paste(\"Sample_\", seq(8), sep = \"\"), 2)) )  # create a 'MirnaExperiment' object obj <- MirnaExperiment(     mirnaExpr = mirnaCounts, geneExpr = geneCounts,     samplesMetadata = meta, pairedSamples = TRUE )"},{"path":"/reference/addDifferentialExpression.html","id":null,"dir":"Reference","previous_headings":"","what":"Manually add differential expression results to a MirnaExperiment object — addDifferentialExpression","title":"Manually add differential expression results to a MirnaExperiment object — addDifferentialExpression","text":"function allows add miRNA gene differential expression results MirnaExperiment object. Instead running performMirnaDE() performGeneDE() functions, one allows use differential expression analyses carried ways. Note possible manually add differential expression results just miRNAs just genes. particularly useful order use pipeline implemented MIRit proteomic data expression data deriving different technologies.","code":""},{"path":"/reference/addDifferentialExpression.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Manually add differential expression results to a MirnaExperiment object — addDifferentialExpression","text":"","code":"addDifferentialExpression(   mirnaObj,   mirnaDE = NULL,   geneDE = NULL,   mirna.logFC = 1,   mirna.pCutoff = 0.05,   mirna.pAdjustment = \"fdr\",   gene.logFC = 1,   gene.pCutoff = 0.05,   gene.pAdjustment = \"fdr\" )"},{"path":"/reference/addDifferentialExpression.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Manually add differential expression results to a MirnaExperiment object — addDifferentialExpression","text":"mirnaObj MirnaExperiment object containing miRNA gene data mirnaDE data.frame containing output miRNA differential expression analysis. Check details section see required format. Default NULL add miRNA differential expression results geneDE data.frame containing output gene differential expression analysis. Check details section see required format. Default NULL add gene differential expression results mirna.logFC minimum log2 fold change required consider miRNA differentially expressed. Default 1, retain two-fold differences mirna.pCutoff adjusted p-value cutoff use miRNA statistical significance. default value 0.05 mirna.pAdjustment p-value correction method miRNA multiple testing. must one : fdr (default), BH, none, holm, hochberg, hommel, bonferroni, gene.logFC minimum log2 fold change required consider gene differentially expressed. Default 1, retain two-fold differences gene.pCutoff adjusted p-value cutoff use gene statistical significance. default value 0.05 gene.pAdjustment p-value correction method gene multiple testing. must one : fdr (default), BH, none, holm, hochberg, hommel, bonferroni, ","code":""},{"path":"/reference/addDifferentialExpression.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Manually add differential expression results to a MirnaExperiment object — addDifferentialExpression","text":"MirnaExperiment object containing differential expression results. access results, user may run mirnaDE() geneDE() functions miRNAs genes, respectively.","code":""},{"path":"/reference/addDifferentialExpression.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Manually add differential expression results to a MirnaExperiment object — addDifferentialExpression","text":"following paragraphs briefly explain formats needed mirnaDE, geneDE, differential expression parameters.","code":""},{"path":"/reference/addDifferentialExpression.html","id":"mirnade-and-genede","dir":"Reference","previous_headings":"","what":"mirnaDE and geneDE","title":"Manually add differential expression results to a MirnaExperiment object — addDifferentialExpression","text":"mirnaDE geneDE two objects class data.frame containing results miRNA gene differential expression analysis respectively. tables contain differential expression results miRNAs/genes analyzed, just statistically significant species. Note can individually add differential expression results miRNAs genes. instance, possible manually add gene differential expression function, performing miRNA differential expression performMirnaDE() function, vice versa. order add miRNA gene differential expression results, must leave mirnaDE geneDE slots NULL. data.frame objects can used, long : One column containing miRNA/gene names (according miRBase/hgnc nomenclature). Accepted column names : ID, Symbol, Gene_Symbol, Mirna, mir, Gene, gene.symbol, Gene.symbol; One column log2 fold changes. Accepted column names : logFC, log2FoldChange, FC, lFC; One column average expression. Accepted column names : AveExpr, baseMean, logCPM; One column p-values resulting differential expression analysis. Accepted column names : P.Value, pvalue, PValue, Pvalue; One column containing p-values adjusted multiple testing. Accepted column names : adj.P.Val, padj, FDR, fdr, adj, adj.p, adjp.","code":""},{"path":"/reference/addDifferentialExpression.html","id":"differential-expression-cutoffs","dir":"Reference","previous_headings":"","what":"Differential expression cutoffs","title":"Manually add differential expression results to a MirnaExperiment object — addDifferentialExpression","text":"mirna.logFC, mirna.pCutoff, mirna.pAdjustment, gene.logFC, gene.pCutoff, gene.pAdjustment represent parameters used define significance differential expression results. needed order inform MIRit features considered differentially expressed.","code":""},{"path":"/reference/addDifferentialExpression.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Manually add differential expression results to a MirnaExperiment object — addDifferentialExpression","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/addDifferentialExpression.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Manually add differential expression results to a MirnaExperiment object — addDifferentialExpression","text":"","code":"# load example data data(geneCounts, package = \"MIRit\") data(mirnaCounts, package = \"MIRit\")  # create samples metadata meta <- data.frame(     \"primary\" = colnames(geneCounts),     \"mirnaCol\" = colnames(mirnaCounts), \"geneCol\" = colnames(geneCounts),     \"disease\" = c(rep(\"PTC\", 8), rep(\"NTH\", 8)),     \"patient\" = c(rep(paste(\"Sample_\", seq(8), sep = \"\"), 2)) )  # create a 'MirnaExperiment' object obj <- MirnaExperiment(     mirnaExpr = mirnaCounts, geneExpr = geneCounts,     samplesMetadata = meta, pairedSamples = TRUE )  # perform miRNA DE with DESeq2 separately dds_m <- DESeq2::DESeqDataSetFromMatrix(     countData = mirnaCounts, colData = meta, design = ~ 0 + disease + patient ) #> Warning: some variables in design formula are characters, converting to factors dds_m <- DESeq2::DESeq(dds_m) #> estimating size factors #> estimating dispersions #> gene-wise dispersion estimates #> mean-dispersion relationship #> final dispersion estimates #> fitting model and testing de_m <- as.data.frame(DESeq2::results(dds_m,     contrast = c(\"disease\", \"PTC\", \"NTH\"), pAdjustMethod = \"fdr\" ))  # perform gene DE with DESeq2 separately dds_g <- DESeq2::DESeqDataSetFromMatrix(     countData = geneCounts, colData = meta, design = ~ 0 + disease + patient ) #> Warning: some variables in design formula are characters, converting to factors dds_g <- DESeq2::DESeq(dds_g) #> estimating size factors #> estimating dispersions #> gene-wise dispersion estimates #> mean-dispersion relationship #> final dispersion estimates #> fitting model and testing de_g <- as.data.frame(DESeq2::results(dds_g,     contrast = c(\"disease\", \"PTC\", \"NTH\"), pAdjustMethod = \"fdr\" ))  # prepare DE tables de_m$ID <- rownames(de_m) de_m <- na.omit(de_m) de_g$ID <- rownames(de_g) de_g <- na.omit(de_g)  # add DE results to MirnaExperiment object obj <- addDifferentialExpression(obj, de_m, de_g,     mirna.logFC = 1, mirna.pCutoff = 0.05,     gene.logFC = 1, gene.pCutoff = 0.05 )"},{"path":"/reference/augmentedPathways.html","id":null,"dir":"Reference","previous_headings":"","what":"Access the miRNA-augmented pathways that were used during TAIPA — augmentedPathways","title":"Access the miRNA-augmented pathways that were used during TAIPA — augmentedPathways","text":"function accesses pathways slot FunctionalEnrichment object returns list object augmented pathways considered topologicalAnalysis() function perform integrative analysis.","code":""},{"path":"/reference/augmentedPathways.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access the miRNA-augmented pathways that were used during TAIPA — augmentedPathways","text":"","code":"augmentedPathways(object)"},{"path":"/reference/augmentedPathways.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access the miRNA-augmented pathways that were used during TAIPA — augmentedPathways","text":"object object class IntegrativePathwayAnalysis containing results miRNA-mRNA pathway analysis","code":""},{"path":"/reference/augmentedPathways.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access the miRNA-augmented pathways that were used during TAIPA — augmentedPathways","text":"list object miRNA-augmented biological pathways.","code":""},{"path":"/reference/augmentedPathways.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Access the miRNA-augmented pathways that were used during TAIPA — augmentedPathways","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/augmentedPathways.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access the miRNA-augmented pathways that were used during TAIPA — augmentedPathways","text":"","code":"# load the example IntegrativePathwayAnalysis object obj <- loadExamples(\"IntegrativePathwayAnalysis\")  # extract the pathways ps <- augmentedPathways(obj)"},{"path":"/reference/batchCorrection.html","id":null,"dir":"Reference","previous_headings":"","what":"Correct for batch effects in miRNA and gene expression measurements — batchCorrection","title":"Correct for batch effects in miRNA and gene expression measurements — batchCorrection","text":"function allows remove unwanted batch effects miRNA gene expression matrices. particular, function fits linear model miRNA/gene expression levels, removes variability caused batch effects. Furthermore, weighted surrogate variable analysis (WSVA) can also included remove effects due surrogate variables. batch effects present, crucial remove function moving correlation analysis.","code":""},{"path":"/reference/batchCorrection.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Correct for batch effects in miRNA and gene expression measurements — batchCorrection","text":"","code":"batchCorrection(   mirnaObj,   assay,   batch = NULL,   batch2 = NULL,   covariates = NULL,   includeWsva = FALSE,   n.sv = 1L,   weight.by.sd = TRUE )"},{"path":"/reference/batchCorrection.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Correct for batch effects in miRNA and gene expression measurements — batchCorrection","text":"mirnaObj MirnaExperiment object containing miRNA gene data assay expression matrix correct. must one genes microRNA batch must name variable present colData MirnaExperiment object (eg. \"disease\"), , alternatively, must character/factor object defines batch memberships. See details section additional information batch2 must name variable present colData MirnaExperiment object (eg. \"disease\"), , alternatively, must character/factor object defines another series batches additive effects specified batch. See details section additional information covariates Additional numeric covariates want correct . must character vector containing names numeric variables present colData MirnaExperiment object (eg. c(\"age\", \"RIN\", \"quantity\")), , alternatively, must simple matrix object. See details section additional information includeWsva Logical, whether correct surrogate variables . Default FALSE n.sv number surrogate variables estimate weight..sd Logical, whether specifically tune surrogate variables variable genes . Default TRUE","code":""},{"path":"/reference/batchCorrection.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Correct for batch effects in miRNA and gene expression measurements — batchCorrection","text":"MirnaExperiment object containing batch effect-corrected expression matrices.","code":""},{"path":"/reference/batchCorrection.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Correct for batch effects in miRNA and gene expression measurements — batchCorrection","text":"Batch effects consist unwanted sources technical variation confound expression variability limit downstream analyses. Since reliability biological conclusions integrative miRNA-mRNA analyses depends association miRNA gene expression levels, pivotal ensure expression measurements affected technical variations. regard, batch effects noticed data, user run function using mirnaIntegration() function perform correlation analysis. Usually, given MirnaExperiment object, user specify: assay want remove batch effects (one genes microRNA); batch variable, variable defines different batches; batch2 variable, can included correct second series batches additive effects specified batch; covariates variables, allows correction one continuous numeric effects. particular, batch batch2 provided names covariates included colData MirnaExperiment object. Alternatively, can character/factor objects declare batch memberships. Similarly, covariates can supplied vector containing names numeric variables listed colData MirnaExperiment objects, can provided simple matrix. Additionally, influence unknown sources technical variation can removed including surrogate variables estimated WSVA. , can set includeWsva TRUE, can specify number surrogate variables use n.sv parameter. , surrogate variables can tuned variable genes setting weight..sd TRUE. Please note recommend remove batch effects directly expression measurements prior correlation analysis. function used remove batch effects differential expression analysis, purpose, better include batch variables linear model. way, underestimate residual degrees freedom, calculated standard errors, t-statistics p-values overoptimistic.","code":""},{"path":"/reference/batchCorrection.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Correct for batch effects in miRNA and gene expression measurements — batchCorrection","text":"estimate surrogate variables remove batch effects expression data, MIRit uses limma::wsva() limma::removeBatchEffect() functions, respectively.","code":""},{"path":"/reference/batchCorrection.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Correct for batch effects in miRNA and gene expression measurements — batchCorrection","text":"Ritchie , Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015). “limma powers differential expression analyses RNA-sequencing microarray studies.” Nucleic Acids Research, 43(7), e47. doi:10.1093/nar/gkv007.","code":""},{"path":"/reference/batchCorrection.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Correct for batch effects in miRNA and gene expression measurements — batchCorrection","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/batchCorrection.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Correct for batch effects in miRNA and gene expression measurements — batchCorrection","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # correct batch effects due to the patient from miRNA expression matrix obj <- batchCorrection(obj, \"microRNA\", batch = \"patient\")"},{"path":"/reference/deAccessors.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract differentially expressed miRNAs and genes from a MirnaExperiment object — deAccessors","title":"Extract differentially expressed miRNAs and genes from a MirnaExperiment object — deAccessors","text":"mirnaDE() geneDE() two accessor functions mirnaDE geneDE slots MirnaExperiment class, respectively. Thus, can used explore results miRNA gene differential expression analysis stored MirnaExperiment object.","code":""},{"path":"/reference/deAccessors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract differentially expressed miRNAs and genes from a MirnaExperiment object — deAccessors","text":"","code":"mirnaDE(object, onlySignificant = TRUE, param = FALSE, returnObject = FALSE)  geneDE(object, onlySignificant = TRUE, param = FALSE, returnObject = FALSE)"},{"path":"/reference/deAccessors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract differentially expressed miRNAs and genes from a MirnaExperiment object — deAccessors","text":"object MirnaExperiment object containing miRNA gene data onlySignificant Logical, TRUE differential expression results returned just statistically significant miRNAs/genes, FALSE full table miRNA/gene differential expression provided. Default TRUE report significant miRNAs/genes param Logical, whether return complete list object parameters used, just results stored data. Default FALSE returnObject Logical, TRUE function return limma/edgeR/DESeq2 object used differential expression analysis","code":""},{"path":"/reference/deAccessors.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract differentially expressed miRNAs and genes from a MirnaExperiment object — deAccessors","text":"data.frame miRNA/gene differential expression, list object parameters used param = TRUE.","code":""},{"path":"/reference/deAccessors.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Extract differentially expressed miRNAs and genes from a MirnaExperiment object — deAccessors","text":"mirnaDE(): Extract miRNA differential expression results geneDE(): Extract gene differential expression results","code":""},{"path":"/reference/deAccessors.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract differentially expressed miRNAs and genes from a MirnaExperiment object — deAccessors","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/deAccessors.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract differentially expressed miRNAs and genes from a MirnaExperiment object — deAccessors","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # access miRNA differential expression of a MirnaExperiment object sig <- mirnaDE(obj) all <- mirnaDE(obj, onlySignificant = FALSE)  # access gene differential expression of a MirnaExperiment object sig <- geneDE(obj) all <- geneDE(obj, onlySignificant = FALSE)"},{"path":"/reference/deAnalysis.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform differential expression analysis — deAnalysis","title":"Perform differential expression analysis — deAnalysis","text":"performMirnaDE() performGeneDE() two functions provided MIRit conduct miRNA gene differential expression analysis, respectively. particular, functions allow user compute differential expression different methods, namely edgeR, DESeq2, limma-voom limma. Data deriving NGS experiments microarray technology suitable functions. precise indications use functions, please refer details section.","code":""},{"path":"/reference/deAnalysis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform differential expression analysis — deAnalysis","text":"","code":"performMirnaDE(   mirnaObj,   group,   contrast,   design,   method = \"edgeR\",   logFC = 1,   pCutoff = 0.05,   pAdjustment = \"fdr\",   filterByExpr.args = list(),   calcNormFactors.args = list(),   estimateDisp.args = list(robust = TRUE),   glmQLFit.args = list(),   glmQLFTest.args = list(),   DESeq.args = list(),   useVoomWithQualityWeights = TRUE,   voom.args = list(),   lmFit.args = list(),   eBayes.args = list(),   useArrayWeights = TRUE,   useWsva = FALSE,   wsva.args = list(),   arrayWeights.args = list(),   useDuplicateCorrelation = FALSE,   correlationBlockVariable = NULL,   duplicateCorrelation.args = list() )  performGeneDE(   mirnaObj,   group,   contrast,   design,   method = \"edgeR\",   logFC = 1,   pCutoff = 0.05,   pAdjustment = \"fdr\",   filterByExpr.args = list(),   calcNormFactors.args = list(),   estimateDisp.args = list(robust = TRUE),   glmQLFit.args = list(),   glmQLFTest.args = list(),   DESeq.args = list(),   useVoomWithQualityWeights = TRUE,   voom.args = list(),   lmFit.args = list(),   eBayes.args = list(),   useArrayWeights = TRUE,   useWsva = FALSE,   wsva.args = list(),   arrayWeights.args = list(),   useDuplicateCorrelation = FALSE,   correlationBlockVariable = NULL,   duplicateCorrelation.args = list() )"},{"path":"/reference/deAnalysis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform differential expression analysis — deAnalysis","text":"mirnaObj MirnaExperiment object containing miRNA gene data group variable interest differential expression analysis. must column name variable present metadata (colData) MirnaExperiment object. See details section additional information contrast character object specifies groups compared differential expression analysis, separated dash (e.g. 'disease-healthy'). Note reference group must last one, additional information see details section design R formula indicates model fit. must include variable interest (group) together eventual covariates (e.g. '~ 0 + disease + sex'). Please note group variable must first one. See details section additional information method statistical package used compute differential expression. NGS experiments, must one edgeR (default), DESeq2, voom (limma-voom). Instead, microarray data, limma can used logFC minimum log2 fold change required consider gene differentially expressed. Default 1, retain two-fold differences pCutoff adjusted p-value cutoff use statistical significance. default value 0.05 pAdjustment p-value correction method multiple testing. must one : fdr (default), BH, none, holm, hochberg, hommel, bonferroni, filterByExpr.args list object containing additional arguments passed edgeR::filterByExpr() function. used method set edgeR voom calcNormFactors.args list object containing additional arguments passed edgeR::calcNormFactors() function. used method set edgeR voom estimateDisp.args list object containing additional arguments passed edgeR::estimateDisp() function. used method set edgeR. Default list(robust = TRUE) use robust parameter glmQLFit.args list object containing additional arguments passed edgeR::glmQLFit() function. used method set edgeR glmQLFTest.args list object containing additional arguments passed edgeR::glmQLFTest() function. used method set edgeR DESeq.args list object containing additional arguments passed DESeq2::DESeq() function. used method set DESeq useVoomWithQualityWeights Logical, whether use limma::voomWithQualityWeights() function just limma::voom() function. used method set voom. Default TRUE voom.args list object containing additional arguments passed limma::voom() function limma::voomWithQualityWeights() function. used method set voom lmFit.args list object containing additional arguments passed limma::lmFit() function. used method set voom limma eBayes.args list object containing additional arguments passed limma::eBayes() function. used method set voom limma useArrayWeights Logical, whether use limma::arrayWeights() function . used method set limma. Default TRUE useWsva Logical, whether use limma::wsva() function . used method set limma. Default FALSE wsva.args list object containing additional arguments passed limma::wsva() function. used method set limma arrayWeights.args list object containing additional arguments passed limma::arrayWeights() function. used method set limma useDuplicateCorrelation Logical, whether use limma::duplicateCorrelation() function . used method set limma. Default FALSE correlationBlockVariable blocking variable use limma::duplicateCorrelation(). Default NULL duplicateCorrelation.args list object containing additional arguments passed limma::duplicateCorrelation() function. used method set limma","code":""},{"path":"/reference/deAnalysis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform differential expression analysis — deAnalysis","text":"MirnaExperiment object containing differential expression results. access results, user may run mirnaDE() geneDE() functions miRNAs genes, respectively.","code":""},{"path":"/reference/deAnalysis.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Perform differential expression analysis — deAnalysis","text":"performing differential expression NGS experiments, count matrices detected method parameter must one edgeR, DESeq2, voom. hand, dealing microarray studies, limma can used. calculate differential expression, MIRit must informed variable interest desired contrast. particular, group parameter must name variable present metadata (colData) MirnaExperiment object, specifies variable used compute differential expression analysis, groups indicated contrast. Specifically, contrast must character vector defines levels compare separated dash. example, variable named 'condition', two levels, namely 'disease' 'healthy', can identify differentially expressed genes 'disease' samples compared 'healthy' subjects specifying: group = 'condition' contrast = 'disease-healthy'. Furthermore, user needs specify model fit expression values. , user state model formula design parameter. Please note correct inner working functions, group variable interest must first variable model formula. Moreover, user can include design sources variation specifying covariates taken account. instance, want compare 'disease' subjects 'healthy' individuals, without influence sex differences, may specify design = ~ condition + sex, 'sex' also variable present metadata (colData) mirnaObj. Notably, methods available, user can supply additional arguments functions implemented edgeR, DESeq2 limma. Therefore, user finer control differential expression analysis performed. regard, microarray studies, user may opt include weighted surrogate variable analysis (WSVA) correct unknown sources variation (useWsva = TRUE). Moreover, microarray data, arrayWeights() function limma can used assess differential expression respect array qualities. Also, duplicateCorrelation() function limma may included pipeline order block effect correlated samples. , user must set useDuplicateCorrelation = TRUE, must specify blocking variable correlationBlockVariable parameter. Additionally, using limma-voom, user may estimate voom transformation without quality weights (specifying useVoomWithQualityWeights = TRUE).","code":""},{"path":"/reference/deAnalysis.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Perform differential expression analysis — deAnalysis","text":"performMirnaDE(): Perform differential expression analysis miRNAs performGeneDE(): Perform differential expression analysis genes","code":""},{"path":"/reference/deAnalysis.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Perform differential expression analysis — deAnalysis","text":"Ritchie , Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015). “limma powers differential expression analyses RNA-sequencing microarray studies.” Nucleic Acids Research, 43(7), e47. doi:10.1093/nar/gkv007. Law, CW, Chen, Y, Shi, W, Smyth, GK (2014). \"Voom: precision weights unlock linear model analysis tools RNA-seq read counts\". Genome Biology 15, R29 Robinson MD, McCarthy DJ, Smyth GK (2010). “edgeR: Bioconductor package differential expression analysis digital gene expression data.” Bioinformatics, 26(1), 139-140. doi:10.1093/bioinformatics/btp616. Love MI, Huber W, Anders S (2014). “Moderated estimation fold change dispersion RNA-seq data DESeq2.” Genome Biology, 15, 550. doi:10.1186/s13059-014-0550-8.","code":""},{"path":"/reference/deAnalysis.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Perform differential expression analysis — deAnalysis","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/deAnalysis.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform differential expression analysis — deAnalysis","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # perform miRNA DE with edgeR obj <- performMirnaDE(obj,     group = \"disease\", contrast = \"PTC-NTH\",     design = ~ 0 + disease + patient, method = \"edgeR\" ) #> Performing differential expression analysis with edgeR... #> Differential expression analysis reported 40 significant miRNAs with p < 0.05 (correction: fdr). You can use the 'mirnaDE()' function to access results.  # perform miRNA DE with DESeq2 obj <- performMirnaDE(obj,     group = \"disease\", contrast = \"PTC-NTH\",     design = ~ 0 + disease + patient, method = \"DESeq2\" ) #> Performing differential expression analysis with DESeq2... #> Warning: some variables in design formula are characters, converting to factors #> estimating size factors #> estimating dispersions #> gene-wise dispersion estimates #> mean-dispersion relationship #> final dispersion estimates #> fitting model and testing #> Differential expression analysis reported 58 significant miRNAs with p < 0.05 (correction: fdr). You can use the 'mirnaDE()' function to access results.  # perform gene DE with limma-voom obj <- performGeneDE(obj,     group = \"disease\", contrast = \"PTC-NTH\",     design = ~ 0 + disease + patient, method = \"voom\" ) #> Performing differential expression analysis with voom... #> Differential expression analysis reported 260 significant genes with p < 0.05 (correction: fdr). You can use the 'geneDE()' function to access results."},{"path":"/reference/enrichGenes.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform functional enrichment analysis of genes — enrichGenes","title":"Perform functional enrichment analysis of genes — enrichGenes","text":"function allows investigate biological functions pathways result dysregulated across biological conditions. particular, different enrichment approaches can used, including -representation analysis (ORA), gene-set enrichment analysis (GSEA), Correlation Adjusted MEan RAnk gene set test (CAMERA). Moreover, analyses, enrichment can carried using different databases, namely Gene Ontology (GO), Kyoto Encyclopedia Genes Genomes (KEGG), MsigDB, WikiPathways, Reactome, Enrichr, Disease Ontology (), Network Cancer Genes (NCG), DisGeNET, COVID19. exhaustive information use function, please refer details section.","code":""},{"path":"/reference/enrichGenes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform functional enrichment analysis of genes — enrichGenes","text":"","code":"enrichGenes(   mirnaObj,   method = \"GSEA\",   database = \"GO\",   category = NULL,   organism = \"Homo sapiens\",   pCutoff = 0.05,   pAdjustment = \"fdr\",   minSize = 10L,   maxSize = 500L,   rankMetric = \"signed.pval\",   eps = 1e-50 )"},{"path":"/reference/enrichGenes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform functional enrichment analysis of genes — enrichGenes","text":"mirnaObj MirnaExperiment object containing miRNA gene data method functional enrichment analysis perform. must one ORA, GSEA (default), CAMERA. additional information, see details section database name database used enrichment analysis. must one : GO, KEGG, MsigDB, WikiPathways, Reactome, Enrichr, , NCG, DisGeNET, COVID19. Default GO category desired subcategory gene sets present database. Please, see details section check available categories database. Default NULL use default categories organism name organism consideration. different databases different supported organisms. see list supported organisms given database, use supportedOrganisms() function. Default Homo sapiens pCutoff adjusted p-value cutoff use statistical significance. default value 0.05 pAdjustment p-value correction method multiple testing. must one : fdr (default), BH, none, holm, hochberg, hommel, bonferroni, minSize minimum size gene set. gene sets containing less number genes considered. Default 10 maxSize maximum size gene set. gene sets containing number genes considered. Default 500 rankMetric ranking statistic used order genes performing GSEA. must one signed.pval (default), logFC, log.pval. additional information, refer details section eps lower boundary p-value calculation (default 1e-50). compute exact p-values, parameter can set 0, even though analysis slower","code":""},{"path":"/reference/enrichGenes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform functional enrichment analysis of genes — enrichGenes","text":"method GSEA CAMERA, function produces object class FunctionalEnrichment containing enrichment results. Instead, ORA used, function returns list object two elements, namely 'upregulated' 'downregulated', containing FunctionalEnrichment object storing enrichment results upregulated downregulated genes, respectively. access results FunctionalEnrichment objects, user can use enrichmentResults() function. Additionally, MIRit provides several functions graphically represent enrichment analyses, including enrichmentBarplot(), enrichmentDotplot(), gseaPlot(), gseaRidgeplot().","code":""},{"path":[]},{"path":"/reference/enrichGenes.html","id":"enrichment-method","dir":"Reference","previous_headings":"","what":"Enrichment method","title":"Perform functional enrichment analysis of genes — enrichGenes","text":"method used functional enrichment analysis drastically influence biological results, thus, must carefully chosen. ORA (Boyle et al., 2004) takes differentially expressed genes (separately considering upregulated downregulated features) uses hypergeometric test infer biological processes regulated genes expected chance. downside approach consider genes passed pre-defined threshold, thus losing slight changes gene expression may important biological consequences. address limit, GSEA introduced (Subramanian, 2005). analysis starts ranking genes according specific criterion, uses running statistic able identify even slight coordinated expression changes genes belonging specific pathway. Therefore, GSEA default method used MIRit perform functional enrichment analysis genes. Moreover, addition ORA GSEA, function allows perform enrichment analysis CAMERA (Wu Smyth, 2012), another competitive test used functional enrichment genes. main advantage method adjusts gene set test statistic according inter-gene correlations. particularly interesting since demonstrated inter-gene correlations may affect reliability functional enrichment analyses.","code":""},{"path":"/reference/enrichGenes.html","id":"databases-and-categories","dir":"Reference","previous_headings":"","what":"Databases and categories","title":"Perform functional enrichment analysis of genes — enrichGenes","text":"Regarding gene sets, multiple databases can used investigate consequences gene expression alterations. However, different databases also includes several subcategories different annotations. specifically query desired categories, category parameter used. reference, listed available categories different databases supported: Gene Ontology (GO): bp, GO - Biological Processes; mf, GO - Molecular Function; cc, GO - Cellular Component; Kyoto Encyclopedia Genes Genomes (KEGG): pathway, KEGG biological pathways; module, KEGG reaction modules; enzyme, KEGG enzyme nomenclature; disease, KEGG diseases (Homo sapiens supported); drug, KEGG drug targets (Homo sapiens supported); network, KEGG disease/drug perturbation netowrks (Homo sapiens supported); MsigDB: H, MsigDB hallmark genes specific biological states/processes; C1, gene sets human chromosome cytogenetic bands; C2-CGP, expression signatures genetic chemical perturbations; C2-CP-BIOCARTA, canonical pathways gene sets derived BioCarta pathway database; C2-CP-KEGG, canonical pathways gene sets derived KEGG pathway database; C2-CP-PID, canonical pathways gene sets derived PID pathway database; C2-CP-REACTOME, canonical pathways gene sets derived Reactome pathway database; C2-CP-WIKIPATHWAYS, canonical pathways gene sets derived WikiPathways database; C3-MIR-MIRDB, gene sets containing high-confidence gene-level predictions human miRNA targets catalogued miRDB v6.0 algorithm; C3-MIR-MIR_Legacy, older gene sets contain genes sharing putative target sites human mature miRNA 3'-UTRs; C3-TFT-GTRD, genes share GTRD predicted transcription factor binding sites region -1000,+100 bp around TSS indicated transcription factor; C3-TFT-TFT_Legacy, older gene sets share upstream cis-regulatory motifs can function potential transcription factor binding sites; C4-CGN, gene sets defined expression neighborhoods centered 380 cancer-associated genes; C4-CM, cancer modules defined Segal et al. 2004; C5-GO-BP, GO - biological process ontology; C5-GO-CC, GO - cellular component ontology; C5-GO-MF, GO - molecular function ontology; C5-HPO, Human Phenotype ontology (HPO); C6, gene sets represent signatures cellular pathways often dis-regulated cancer; C7-IMMUNESIGDB, manually curated gene sets representing chemical genetic perturbations immune system; C7-VAX, gene sets deriving Human Immunology Project Consortium (HIPC) describing human transcriptomic immune responses vaccinations; C8, gene sets contain curated cluster markers cell types; WikiPathways; Reactome; Enrichr: avaliable gene sets can listed geneset::enrichr_metadata Disease Ontology (); Network Cancer Genes (NCG): v6, sixth version; v7, seventh version; DisGeNET; COVID-19.","code":""},{"path":"/reference/enrichGenes.html","id":"supported-organisms","dir":"Reference","previous_headings":"","what":"Supported organisms","title":"Perform functional enrichment analysis of genes — enrichGenes","text":"database, different organisms supported. check supported organisms given database, MIRit provides supportedOrganisms() function.","code":""},{"path":"/reference/enrichGenes.html","id":"gsea-ranking-statistic","dir":"Reference","previous_headings":"","what":"GSEA ranking statistic","title":"Perform functional enrichment analysis of genes — enrichGenes","text":"ranking statistic used order genes conducting GSEA able influence biological interpretation functional enrichment results. Several metrics used scientific literature. MIRit implements possibility using signed.pval, logFC, log.pval. particular, simplest option rank genes according logFC value. However, procedure biased higher variance lowly abundant genes. Therefore, recommend use signed.pval metric, consists p-value gene multiplied sign logFC, .e. sign(logFC) * p-value. Alternatively, log,pval metric, consist product logFC p-value, .e. logFC * p-value can also used.","code":""},{"path":"/reference/enrichGenes.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Perform functional enrichment analysis of genes — enrichGenes","text":"download gene sets mentioned databases, MIRit uses geneset R package. Moreover, perform ORA GSEA, MIRit implements fgsea algorithm, whereas CAMERA, limma package used.","code":""},{"path":"/reference/enrichGenes.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Perform functional enrichment analysis of genes — enrichGenes","text":"Liu, Y., Li, G. Empowering biologists decode omics data: Genekitr R package web server. BMC Bioinformatics 24, 214 (2023). https://doi.org/10.1186/s12859-023-05342-9. Korotkevich G, Sukhov V, Sergushichev (2019). “Fast gene set enrichment analysis.” bioRxiv. doi:10.1101/060012, http://biorxiv.org/content/early/2016/06/20/060012. Ritchie , Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015). “limma powers differential expression analyses RNA-sequencing microarray studies.” Nucleic Acids Research, 43(7), e47. doi:10.1093/nar/gkv007. Wu D, Smyth GK. Camera: competitive gene set test accounting inter-gene correlation. Nucleic Acids Res. 2012 Sep 1;40(17):e133. doi: 10.1093/nar/gks461. Epub 2012 May 25. PMID: 22638577; PMCID: PMC3458527.","code":""},{"path":"/reference/enrichGenes.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Perform functional enrichment analysis of genes — enrichGenes","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/enrichGenes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform functional enrichment analysis of genes — enrichGenes","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # perform GSEA with KEGG de_enr <- enrichGenes(obj, method = \"GSEA\", database = \"KEGG\") #> Since not specified, 'category' for KEGG database is set to pathway (default). #> Preparing the appropriate gene set... #>  #> Some ID occurs one-to-many match, like \"79154, 7920, 79143\"... #> 99.96% genes are mapped to symbol #> Ranking genes based on signed.pval... #> Performing gene-set enrichment analysis (GSEA)... #> GSEA reported 2 significantly enriched terms.  # extract results de_df <- enrichmentResults(de_enr)  # create a dotplot of enriched terms enrichmentDotplot(de_enr)"},{"path":"/reference/enrichTargets.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform an enrichment analysis of integrated microRNA targets — enrichTargets","title":"Perform an enrichment analysis of integrated microRNA targets — enrichTargets","text":"function allows perform -representation analysis (ORA) integrated miRNA targets order explore biological effects targets statistically associated/correlated DE-miRNAs. enrichment analysis can performed using different databases, namely Gene Ontology (GO), Kyoto Encyclopedia Genes Genomes (KEGG), MsigDB, WikiPathways, Reactome, Enrichr, Disease Ontology (), Network Cancer Genes (NCG), DisGeNET, COVID19.","code":""},{"path":"/reference/enrichTargets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform an enrichment analysis of integrated microRNA targets — enrichTargets","text":"","code":"enrichTargets(   mirnaObj,   database = \"GO\",   category = NULL,   organism = \"Homo sapiens\",   pCutoff = 0.05,   pAdjustment = \"fdr\",   minSize = 10L,   maxSize = 500L )"},{"path":"/reference/enrichTargets.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform an enrichment analysis of integrated microRNA targets — enrichTargets","text":"mirnaObj MirnaExperiment object containing miRNA gene data database name database used enrichment analysis. must one : GO, KEGG, MsigDB, WikiPathways, Reactome, Enrichr, , NCG, DisGeNET, COVID19. Default GO category desired subcategory gene sets present database. Please, see details section check available categories database. Default NULL use default categories organism name organism consideration. different databases different supported organisms. see list supported organisms given database, use supportedOrganisms() function. Default Homo sapiens pCutoff adjusted p-value cutoff use statistical significance. default value 0.05 pAdjustment p-value correction method multiple testing. must one : fdr (default), BH, none, holm, hochberg, hommel, bonferroni, minSize minimum size gene set. gene sets containing less number genes considered. Default 10 maxSize maximum size gene set. gene sets containing number genes considered. Default 500","code":""},{"path":"/reference/enrichTargets.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform an enrichment analysis of integrated microRNA targets — enrichTargets","text":"function produces list object two elements, namely 'upregulated' 'downregulated', containing FunctionalEnrichment object storing enrichment results upregulated downregulated target genes, respectively. access results FunctionalEnrichment objects, user can use enrichmentResults() function. Additionally, MIRit provides several functions graphically represent enrichment analyses, including enrichmentBarplot(), enrichmentDotplot().","code":""},{"path":"/reference/enrichTargets.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Perform an enrichment analysis of integrated microRNA targets — enrichTargets","text":"database, different organisms supported. check supported organisms given database, MIRit provides supportedOrganisms() function. Moreover, since different database support multiple subcategories, category parameter can set specify desired resource. specific information regarding available categories different databases, check details section enrichGenes() documentation.","code":""},{"path":"/reference/enrichTargets.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Perform an enrichment analysis of integrated microRNA targets — enrichTargets","text":"download gene sets mentioned databases, MIRit uses geneset R package. Moreover, perform ORA, MIRit implements fgsea package Bioconductor.","code":""},{"path":"/reference/enrichTargets.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Perform an enrichment analysis of integrated microRNA targets — enrichTargets","text":"Liu, Y., Li, G. Empowering biologists decode omics data: Genekitr R package web server. BMC Bioinformatics 24, 214 (2023). https://doi.org/10.1186/s12859-023-05342-9. Korotkevich G, Sukhov V, Sergushichev (2019). “Fast gene set enrichment analysis.” bioRxiv. doi:10.1101/060012, http://biorxiv.org/content/early/2016/06/20/060012.","code":""},{"path":"/reference/enrichTargets.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Perform an enrichment analysis of integrated microRNA targets — enrichTargets","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/enrichTargets.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform an enrichment analysis of integrated microRNA targets — enrichTargets","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # perform enrichment analysis of integrated targets with DO targets_enrichment <- enrichTargets(obj, database = \"DO\") #> Preparing the appropriate gene set... #> Some ID occurs one-to-many match, like \"26476, 127068, 101060321\"... #> 99.06% genes are mapped to symbol #> Performing the enrichment of upregulated genes... #> Performing the enrichment of downregulated genes... #> The enrichment of genes reported 113 significantly enriched terms for downregulated genes and 0 for upregulated genes.  # extract enrichment results of downregulated targets enr_down <- targets_enrichment[[\"downregulated\"]]  # extract enrichment results as a data.frame enr_df <- enrichmentResults(enr_down)  # create a dotplot of enriched terms enrichmentDotplot(enr_down)"},{"path":"/reference/enrichedFeatures.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the names of the pre-ranked features in a GSEA experiment — enrichedFeatures","title":"Extract the names of the pre-ranked features in a GSEA experiment — enrichedFeatures","text":"function accesses features slot FunctionalEnrichment object returns character vector names features considered GSEA order ranking metric.","code":""},{"path":"/reference/enrichedFeatures.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the names of the pre-ranked features in a GSEA experiment — enrichedFeatures","text":"","code":"enrichedFeatures(object)"},{"path":"/reference/enrichedFeatures.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the names of the pre-ranked features in a GSEA experiment — enrichedFeatures","text":"object object class FunctionalEnrichment containing enrichment results","code":""},{"path":"/reference/enrichedFeatures.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the names of the pre-ranked features in a GSEA experiment — enrichedFeatures","text":"character vector names genes ordered based ranking metric.","code":""},{"path":"/reference/enrichedFeatures.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract the names of the pre-ranked features in a GSEA experiment — enrichedFeatures","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/enrichedFeatures.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the names of the pre-ranked features in a GSEA experiment — enrichedFeatures","text":"","code":"# load example FunctionalEnrichment object obj <- loadExamples(\"FunctionalEnrichment\")  # extract the ranking metric rmet <- enrichmentMetric(obj)  ## extract the corresponding names rnames <- enrichedFeatures(obj)"},{"path":"/reference/enrichmentBarplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a barplot for functional enrichment analysis — enrichmentBarplot","title":"Create a barplot for functional enrichment analysis — enrichmentBarplot","text":"function produces barplot show results functional enrichment analyses carried -representation analysis (ORA), gene set enrichment analysis (GSEA), competitive gene set test accounting inter-gene correlation (CAMERA). particular, function can take input enrichment results generated enrichGenes() function.","code":""},{"path":"/reference/enrichmentBarplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a barplot for functional enrichment analysis — enrichmentBarplot","text":"","code":"enrichmentBarplot(   enrichment,   showTerms = 10,   showTermsParam = \"ratio\",   splitDir = TRUE,   title = NULL )"},{"path":"/reference/enrichmentBarplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a barplot for functional enrichment analysis — enrichmentBarplot","text":"enrichment object class FunctionalEnrichment containing enrichment results showTerms number terms shown, based order determined parameter showTermsParam; , alternatively, character vector indicating terms plot. Default 10 showTermsParam order top terms selected specified showTerms parameter. must one ratio (default), padj, pval overlap splitDir Logical, TRUE resulting plot divided two columns basis enrichment direction (). Default TRUE. applies enrichment method GSEA CAMERA title title plot. Default NULL include plot title","code":""},{"path":"/reference/enrichmentBarplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a barplot for functional enrichment analysis — enrichmentBarplot","text":"ggplot graph barplot enrichment results.","code":""},{"path":"/reference/enrichmentBarplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a barplot for functional enrichment analysis — enrichmentBarplot","text":"producing barplot function, significant pathways ordered x-axis basis ratio number overlapping genes set, total number genes set. Moreover, color scale dots relative adjusted p-values category.","code":""},{"path":"/reference/enrichmentBarplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create a barplot for functional enrichment analysis — enrichmentBarplot","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/enrichmentBarplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a barplot for functional enrichment analysis — enrichmentBarplot","text":"","code":"# load example FunctionalEnrichment object obj <- loadExamples(\"FunctionalEnrichment\")  # extract results res <- enrichmentResults(obj)  # plot results enrichmentBarplot(obj)"},{"path":"/reference/enrichmentDatabase.html","id":null,"dir":"Reference","previous_headings":"","what":"Access the database used for functional enrichment analyses — enrichmentDatabase","title":"Access the database used for functional enrichment analyses — enrichmentDatabase","text":"function accesses database slot FunctionalEnrichment object returns name database used enrichGenes() function perform enrichment analysis.","code":""},{"path":"/reference/enrichmentDatabase.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access the database used for functional enrichment analyses — enrichmentDatabase","text":"","code":"enrichmentDatabase(object)"},{"path":"/reference/enrichmentDatabase.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access the database used for functional enrichment analyses — enrichmentDatabase","text":"object object class FunctionalEnrichment containing enrichment results","code":""},{"path":"/reference/enrichmentDatabase.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access the database used for functional enrichment analyses — enrichmentDatabase","text":"character containing name database, KEGG.","code":""},{"path":"/reference/enrichmentDatabase.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Access the database used for functional enrichment analyses — enrichmentDatabase","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/enrichmentDatabase.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access the database used for functional enrichment analyses — enrichmentDatabase","text":"","code":"# load example FunctionalEnrichment object obj <- loadExamples(\"FunctionalEnrichment\")  # see the database enrichmentDatabase(obj) #> [1] \"KEGG (category: pathway)\""},{"path":"/reference/enrichmentDotplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a dotplot for functional enrichment analysis — enrichmentDotplot","title":"Create a dotplot for functional enrichment analysis — enrichmentDotplot","text":"function produces dotplot show results functional enrichment analyses carried -representation analysis (ORA), gene set enrichment analysis (GSEA), competitive gene set test accounting inter-gene correlation (CAMERA). particular, function can take input enrichment results generated enrichGenes() function.","code":""},{"path":"/reference/enrichmentDotplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a dotplot for functional enrichment analysis — enrichmentDotplot","text":"","code":"enrichmentDotplot(   enrichment,   showTerms = 10,   showTermsParam = \"ratio\",   splitDir = TRUE,   title = NULL )"},{"path":"/reference/enrichmentDotplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a dotplot for functional enrichment analysis — enrichmentDotplot","text":"enrichment object class FunctionalEnrichment containing enrichment results showTerms number terms shown, based order determined parameter showTermsParam; , alternatively, character vector indicating terms plot. Default 10 showTermsParam order top terms selected specified showTerms parameter. must one ratio (default), padj, pval overlap splitDir Logical, TRUE resulting plot divided two columns basis enrichment direction (). Default TRUE. applies enrichment method GSEA CAMERA title title plot. Default NULL include plot title","code":""},{"path":"/reference/enrichmentDotplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a dotplot for functional enrichment analysis — enrichmentDotplot","text":"ggplot graph dotplot enrichment results.","code":""},{"path":"/reference/enrichmentDotplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a dotplot for functional enrichment analysis — enrichmentDotplot","text":"producing dotplot function, significant pathways ordered x-axis basis ratio number overlapping genes set, total number genes set. Moreover, size dot proportional number overlapping features. Finally, color scale dots relative adjusted p-values category.","code":""},{"path":"/reference/enrichmentDotplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create a dotplot for functional enrichment analysis — enrichmentDotplot","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/enrichmentDotplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a dotplot for functional enrichment analysis — enrichmentDotplot","text":"","code":"# load example FunctionalEnrichment object obj <- loadExamples(\"FunctionalEnrichment\")  # extract results res <- enrichmentResults(obj)  # plot results enrichmentDotplot(obj)"},{"path":"/reference/enrichmentMethod.html","id":null,"dir":"Reference","previous_headings":"","what":"Access the method used for functional enrichment analyses — enrichmentMethod","title":"Access the method used for functional enrichment analyses — enrichmentMethod","text":"function accesses method slot FunctionalEnrichment object returns name enrichment strategy used enrichGenes() function perform enrichment analysis.","code":""},{"path":"/reference/enrichmentMethod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access the method used for functional enrichment analyses — enrichmentMethod","text":"","code":"enrichmentMethod(object)"},{"path":"/reference/enrichmentMethod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access the method used for functional enrichment analyses — enrichmentMethod","text":"object object class FunctionalEnrichment containing enrichment results","code":""},{"path":"/reference/enrichmentMethod.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access the method used for functional enrichment analyses — enrichmentMethod","text":"character containing enrichment method, GSEA.","code":""},{"path":"/reference/enrichmentMethod.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Access the method used for functional enrichment analyses — enrichmentMethod","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/enrichmentMethod.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access the method used for functional enrichment analyses — enrichmentMethod","text":"","code":"# load example FunctionalEnrichment object obj <- loadExamples(\"FunctionalEnrichment\")  # see the method enrichmentMethod(obj) #> [1] \"Gene-Set Enrichment Analysis (GSEA)\""},{"path":"/reference/enrichmentMetric.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the GSEA ranking metric used for functional enrichment analyses — enrichmentMetric","title":"Extract the GSEA ranking metric used for functional enrichment analyses — enrichmentMetric","text":"function accesses statistic slot FunctionalEnrichment object returns numeric vector metric used rank genes GSEA.","code":""},{"path":"/reference/enrichmentMetric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the GSEA ranking metric used for functional enrichment analyses — enrichmentMetric","text":"","code":"enrichmentMetric(object)"},{"path":"/reference/enrichmentMetric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the GSEA ranking metric used for functional enrichment analyses — enrichmentMetric","text":"object object class FunctionalEnrichment containing enrichment results","code":""},{"path":"/reference/enrichmentMetric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the GSEA ranking metric used for functional enrichment analyses — enrichmentMetric","text":"numeric vector containing ranking metric.","code":""},{"path":"/reference/enrichmentMetric.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract the GSEA ranking metric used for functional enrichment analyses — enrichmentMetric","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/enrichmentMetric.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the GSEA ranking metric used for functional enrichment analyses — enrichmentMetric","text":"","code":"# load example FunctionalEnrichment object obj <- loadExamples(\"FunctionalEnrichment\")  # extract the ranking metric rmet <- enrichmentMetric(obj)"},{"path":"/reference/enrichmentResults.html","id":null,"dir":"Reference","previous_headings":"","what":"Access the results of functional enrichment analyses — enrichmentResults","title":"Access the results of functional enrichment analyses — enrichmentResults","text":"function accesses data slot FunctionalEnrichment object returns data.frame enrichment results.","code":""},{"path":"/reference/enrichmentResults.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access the results of functional enrichment analyses — enrichmentResults","text":"","code":"enrichmentResults(object)"},{"path":"/reference/enrichmentResults.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access the results of functional enrichment analyses — enrichmentResults","text":"object object class FunctionalEnrichment containing enrichment results","code":""},{"path":"/reference/enrichmentResults.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access the results of functional enrichment analyses — enrichmentResults","text":"data.frame object containing results functional enrichment analyses, returned enrichGenes() function.","code":""},{"path":"/reference/enrichmentResults.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Access the results of functional enrichment analyses — enrichmentResults","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/enrichmentResults.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access the results of functional enrichment analyses — enrichmentResults","text":"","code":"# load example FunctionalEnrichment object obj <- loadExamples(\"FunctionalEnrichment\")  # extract results de_df <- enrichmentResults(obj)"},{"path":"/reference/findMirnaSNPs.html","id":null,"dir":"Reference","previous_headings":"","what":"Find disease-associated SNPs occurring at DE-miRNA loci — findMirnaSNPs","title":"Find disease-associated SNPs occurring at DE-miRNA loci — findMirnaSNPs","text":"function allows identify disease-associated genomic variants affecting differentially expressed miRNA genes host genes. , function uses gwasrapidd retrieve SNPs-disease associations, retains SNPs affect DE-miRNA genes relative host genes (intronic miRNAs).","code":""},{"path":"/reference/findMirnaSNPs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find disease-associated SNPs occurring at DE-miRNA loci — findMirnaSNPs","text":"","code":"findMirnaSNPs(mirnaObj, diseaseEFO)"},{"path":"/reference/findMirnaSNPs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find disease-associated SNPs occurring at DE-miRNA loci — findMirnaSNPs","text":"mirnaObj MirnaExperiment object containing miRNA gene data diseaseEFO EFO identifier disease interest. can identified searchDisease() function","code":""},{"path":"/reference/findMirnaSNPs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find disease-associated SNPs occurring at DE-miRNA loci — findMirnaSNPs","text":"data.frame containing details disease-SNPs associated differentially expressed miRNAs: variant contains SNP identifiers; gene defines gene affected disease-SNP (may miRNA gene host gene intronic miRNA); miRNA.gene specifies DE-miRNA gene present; miRNA.precursor specifies name miRNA precursor affected disease-SNPs; chr indicates chromosome SNPs; position shows SNP position; allele displays possible alleles SNPs; distance specifies distance SNPs miRNAs; is_upstream indicates whether SNP upstream miRNA gene; is_downstream indicates whether SNP downstream miRNA gene; mirnaStrand shows strand; mirnaStartPosition displays start position DE-miRNA gene; mirnaEndPosition displays end position DE-miRNA gene.","code":""},{"path":"/reference/findMirnaSNPs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Find disease-associated SNPs occurring at DE-miRNA loci — findMirnaSNPs","text":"SNPs occurring within miRNAs may important effects biological function transcripts. Indeed, SNP present within miRNA gene might alter expression spectrum miRNA targets. retrieve disease-SNPs, function uses gwasrapidd package, directly queries NHGRI-EBI Catalog published genome-wide association studies. running function, user can use mirVariantPlot() function produce trackplot visualizing genomic location SNPs within miRNA genes.","code":""},{"path":"/reference/findMirnaSNPs.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Find disease-associated SNPs occurring at DE-miRNA loci — findMirnaSNPs","text":"retrieve disease-associated SNPs, function makes use gwasrapidd package.","code":""},{"path":"/reference/findMirnaSNPs.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Find disease-associated SNPs occurring at DE-miRNA loci — findMirnaSNPs","text":"Ramiro Magno, Ana-Teresa Maia, gwasrapidd: R package query, download wrangle GWAS catalog data, Bioinformatics, Volume 36, Issue 2, January 2020, Pages 649–650, https://doi.org/10.1093/bioinformatics/btz605","code":""},{"path":"/reference/findMirnaSNPs.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Find disease-associated SNPs occurring at DE-miRNA loci — findMirnaSNPs","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/findMirnaSNPs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Find disease-associated SNPs occurring at DE-miRNA loci — findMirnaSNPs","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # \\donttest{ # search disease searchDisease(\"Alzheimer disease\") #> Checking for cached EFO traits... #> Downloading EFO traits, this may take some minutes... #> Searching for disease: Alzheimer disease #> [1] \"Alzheimer's disease biomarker measurement\"  #> [2] \"Alzheimer's disease neuropathologic change\" #> [3] \"Alzheimer disease\"                          #> [4] \"late-onset Alzheimers disease\"              #> [5] \"family history of Alzheimer’s disease\"      #> [6] \"age of onset of Alzheimer disease\"          disId <- \"Alzheimer disease\"  # retrieve associated SNPs association <- findMirnaSNPs(obj, disId) #> Querying GWAS Catalog, this may take some time... #> Finding genomic information of differentially expressed miRNAs... #> No disease-related SNPs are present within DE-miRNA loci. # }"},{"path":"/reference/geneCounts.html","id":null,"dir":"Reference","previous_headings":"","what":"Count matrix for gene expression in thyroid cancer — geneCounts","title":"Count matrix for gene expression in thyroid cancer — geneCounts","text":"dataset contains gene expression matrix resulting RNA-Seq analysis thyroid cancer. Specifically, data originate Riesco-Eizaguirre et al (2015), sequenced 8 papillary thyroid carcinomas (PTC) together paired samples normal thyroid tissue. thing done microRNAs order investigate effects target genes. Data included package obtained Gene Expression Omnibus (GEO accession: GSE63511).","code":""},{"path":"/reference/geneCounts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count matrix for gene expression in thyroid cancer — geneCounts","text":"","code":"data(geneCounts)"},{"path":[]},{"path":"/reference/geneCounts.html","id":"genecounts","dir":"Reference","previous_headings":"","what":"geneCounts","title":"Count matrix for gene expression in thyroid cancer — geneCounts","text":"matrix object containing samples columns genes rows.","code":""},{"path":"/reference/geneCounts.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Count matrix for gene expression in thyroid cancer — geneCounts","text":"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63511","code":""},{"path":"/reference/geneCounts.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Count matrix for gene expression in thyroid cancer — geneCounts","text":"Garcilaso Riesco-Eizaguirre et al., “MiR-146b-3p/PAX8/NIS Regulatory Circuit Modulates Differentiation Phenotype Function Thyroid Cells Carcinogenesis,” Cancer Research 75, . 19 (September 30, 2015): 4119–30, https://doi.org/10.1158/0008-5472.CAN-14-3547.","code":""},{"path":"/reference/geneSet.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the gene-sets used for functional enrichment analyses — geneSet","title":"Extract the gene-sets used for functional enrichment analyses — geneSet","text":"function accesses geneSet slot FunctionalEnrichment object returns list collection genes used enrichment.","code":""},{"path":"/reference/geneSet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the gene-sets used for functional enrichment analyses — geneSet","text":"","code":"geneSet(object)"},{"path":"/reference/geneSet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the gene-sets used for functional enrichment analyses — geneSet","text":"object object class FunctionalEnrichment containing enrichment results","code":""},{"path":"/reference/geneSet.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the gene-sets used for functional enrichment analyses — geneSet","text":"list containing gene-sets.","code":""},{"path":"/reference/geneSet.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract the gene-sets used for functional enrichment analyses — geneSet","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/geneSet.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the gene-sets used for functional enrichment analyses — geneSet","text":"","code":"# load example FunctionalEnrichment object obj <- loadExamples(\"FunctionalEnrichment\")  # extract the gene-sets gs <- geneSet(obj)"},{"path":"/reference/getEvidence.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the scientific evidence for a particular disease-SNP association — getEvidence","title":"Get the scientific evidence for a particular disease-SNP association — getEvidence","text":"function returns biomedical evidence supports association particular SNP phenotypic trait.","code":""},{"path":"/reference/getEvidence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the scientific evidence for a particular disease-SNP association — getEvidence","text":"","code":"getEvidence(variant, diseaseEFO)"},{"path":"/reference/getEvidence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the scientific evidence for a particular disease-SNP association — getEvidence","text":"variant SNP ID particular variant interest (e.g. 'rs394581') diseaseEFO EFO identifier disease interest. can identified function searchDisease()","code":""},{"path":"/reference/getEvidence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the scientific evidence for a particular disease-SNP association — getEvidence","text":"tbl_df dataframe containing information literature evidences disease-SNP association.","code":""},{"path":"/reference/getEvidence.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Get the scientific evidence for a particular disease-SNP association — getEvidence","text":"retrieve evidences disease-SNP association, function makes use gwasrapidd package.","code":""},{"path":"/reference/getEvidence.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Get the scientific evidence for a particular disease-SNP association — getEvidence","text":"Ramiro Magno, Ana-Teresa Maia, gwasrapidd: R package query, download wrangle GWAS catalog data, Bioinformatics, Volume 36, Issue 2, January 2020, Pages 649–650, https://doi.org/10.1093/bioinformatics/btz605.","code":""},{"path":"/reference/getEvidence.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get the scientific evidence for a particular disease-SNP association — getEvidence","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/getEvidence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get the scientific evidence for a particular disease-SNP association — getEvidence","text":"","code":"# \\donttest{ # searchDisease(\"Alzheimer disease\") evidence <- getEvidence(\"rs2075650\", diseaseEFO = \"Alzheimer disease\") #> Retrieving biomedical evidence for the association between Alzheimer disease and rs2075650 variant... #> 109 studies reporting this association were found! # }"},{"path":"/reference/getTargets.html","id":null,"dir":"Reference","previous_headings":"","what":"Get microRNA targets — getTargets","title":"Get microRNA targets — getTargets","text":"function allows obtain human miRNA-target interactions using two databases, namely miRTarBase v9, contains experimentally validated interactions, microRNA Data Integration Portal (mirDIP) database, aggregates miRNA target predictions 24 different resources using integrated score inferred different prediction metrics. way, demonstrated Tokar et al. 2018, mirDIP reports accurate predictions compared individual tools. However, species Homo sapiens validated interactions returned, since mirDIP available human miRNAs.","code":""},{"path":"/reference/getTargets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get microRNA targets — getTargets","text":"","code":"getTargets(   mirnaObj,   organism = \"Homo sapiens\",   score = \"High\",   includeValidated = TRUE )"},{"path":"/reference/getTargets.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get microRNA targets — getTargets","text":"mirnaObj MirnaExperiment object containing miRNA gene data organism specie retrieving miRNA target genes. Available species : Homo sapiens (default), Mus musculus, Rattus norvegicus, Arabidopsis thaliana, Bos taurus, Caenorhabditis elegans, Danio rerio, Drosophila melanogaster, Gallus gallus, Sus scrofa score minimum mirDIP confidence score. must one High, High (default), Medium, Low, correspond ranks among top 1%, top 5% (excluding top 1%), top 1/3 (excluding top 5%) remaining predictions, respectively includeValidated Logical, whether include validated interactions miRTarBase . Default TRUE order retrieve predicted validated targets. Note species Homo sapines validated interactions considered.","code":""},{"path":"/reference/getTargets.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get microRNA targets — getTargets","text":"MirnaExperiment object containing miRNA targets stored targets slot. Results can accessed mirnaTargets() function.","code":""},{"path":"/reference/getTargets.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get microRNA targets — getTargets","text":"define miRNA target genes, can consider experimentally validated computationally predicted interactions. Interactions former type generally preferred, since corroborated biomolecular experiments. However, often sufficient, thus making necessary consider predicted interactions well. downside miRNA target prediction algorithms scarce extend overlap existing different tools. address issue, several ensemble methods developed, trying aggregate predictions obtained different algorithms. Initially, several researchers determined significant miRNA-target pairs predicted one tool (intersection method). However, method able capture important number meaningful interactions. Alternatively, strategies used merge predictions several algorithms (union method). Despite identifying true relationships, union method leads higher proportion false discoveries. Therefore, ensemble methods including mirDIP started using statistics rank miRNA-target predictions obtained multiple algorithms. additional information mirDIP database ranking metric check Tokar et al. 2018 Hauschild et al. 2023. function defines miRNA targets considering validated interactions present miRTarBase (version 9), predicted interactions identified mirDIP. Please note species Homo sapiens, miRTarBase interactions available.","code":""},{"path":"/reference/getTargets.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Get microRNA targets — getTargets","text":"access mirDIP database https://ophid.utoronto.ca/mirDIP/, function directly use mirDIP API R.","code":""},{"path":"/reference/getTargets.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Get microRNA targets — getTargets","text":"Tomas Tokar others, mirDIP 4.1—integrative database human microRNA target predictions, Nucleic Acids Research, Volume 46, Issue D1, 4 January 2018, Pages D360–D370, https://doi.org/10.1093/nar/gkx1144. Anne-Christin Hauschild others, MirDIP 5.2: tissue context annotation novel microRNA curation, Nucleic Acids Research, Volume 51, Issue D1, 6 January 2023, Pages D217–D225, https://doi.org/10.1093/nar/gkac1070. Hsi-Yuan Huang others, miRTarBase update 2022: informative resource experimentally validated miRNA–target interactions, Nucleic Acids Research, Volume 50, Issue D1, 7 January 2022, Pages D222–D230, https://doi.org/10.1093/nar/gkab1079.","code":""},{"path":"/reference/getTargets.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get microRNA targets — getTargets","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/getTargets.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get microRNA targets — getTargets","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # \\donttest{ # retrieve targets obj <- getTargets(mirnaObj = obj) #> Retrieving targets from mirDIP (this may take a while)... #>  Downloading: 5.5 kB      Downloading: 5.5 kB      Downloading: 8.4 kB      Downloading: 8.4 kB      Downloading: 16 kB      Downloading: 16 kB      Downloading: 18 kB      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pathway — gseaPlot","title":"Create a GSEA plot that displays the running enrichment score (ES) for a given pathway — gseaPlot","text":"function creates classic enrichment plot show results gene set enrichment analyses (GSEA). particular, function takes input GSEA results originating enrichGenes() function, returns ggplot2 object GSEA plot. kind plots, running enrichment score (ES) given pathway shown y-axis, whereas gene positions ranked list reported x-axis.","code":""},{"path":"/reference/gseaPlot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a GSEA plot that displays the running enrichment score (ES) for a given pathway — gseaPlot","text":"","code":"gseaPlot(   enrichment,   pathway,   showTitle = TRUE,   rankingMetric = FALSE,   lineColor = \"green\",   lineSize = 1,   vlineColor = \"red\",   vlineSize = 0.6 )"},{"path":"/reference/gseaPlot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a GSEA plot that displays the running enrichment score (ES) for a given pathway — gseaPlot","text":"enrichment object class FunctionalEnrichment containing enrichment results pathway must name significantly enriched term/pathway want produce GSEA plot (e.g. 'Thyroid hormone synthesis') showTitle Logical, whether add name pathway/term plot title. Default TRUE rankingMetric Logical, whether show variations ranking metric plot. Default FALSE lineColor must R color name specifies color running score line. Default green. Available color formats include color names, 'blue' 'red', hexadecimal colors specified #RRGGBB lineSize line width running score line. Default 1 vlineColor must R color name specifies color vertical line indicating enrichment score (ES). Default red. Available color formats include color names, 'blue' 'red', hexadecimal colors specified #RRGGBB vlineSize line width vertical line indicating enrichment score (ES). Default 0.6","code":""},{"path":"/reference/gseaPlot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a GSEA plot that displays the running enrichment score (ES) for a given pathway — gseaPlot","text":"object class ggplot containing GSEA plot.","code":""},{"path":"/reference/gseaPlot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create a GSEA plot that displays the running enrichment score (ES) for a given pathway — gseaPlot","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/gseaPlot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a GSEA plot that displays the running enrichment score (ES) for a given pathway — gseaPlot","text":"","code":"# load example FunctionalEnrichment object obj <- loadExamples(\"FunctionalEnrichment\")  # extract results res <- enrichmentResults(obj)  # plot results gseaPlot(obj, pathway = \"Thyroid hormone synthesis\")"},{"path":"/reference/gseaRidgeplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a ridgeplot to display the results of GSEA analysis — gseaRidgeplot","title":"Create a ridgeplot to display the results of GSEA analysis — gseaRidgeplot","text":"function creates ridgeplot useful showing results GSEA analyses. output function plot enriched terms/pathways found enrichGenes() function visualized basis ranking metric used analysis. resulting areas represent density signed p-values, log2 fold changes, log.p-values belonging genes annotated category.","code":""},{"path":"/reference/gseaRidgeplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a ridgeplot to display the results of GSEA analysis — gseaRidgeplot","text":"","code":"gseaRidgeplot(   enrichment,   showTerms = 10,   showTermsParam = \"padj\",   title = NULL )"},{"path":"/reference/gseaRidgeplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a ridgeplot to display the results of GSEA analysis — gseaRidgeplot","text":"enrichment object class FunctionalEnrichment containing enrichment results showTerms number terms shown, based order determined parameter showTermsParam; , alternatively, character vector indicating terms plot. Default 10 showTermsParam order top terms selected specified showTerms parameter. must one ratio, padj (default), pval overlap title title plot. Default NULL include plot title","code":""},{"path":"/reference/gseaRidgeplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a ridgeplot to display the results of GSEA analysis — gseaRidgeplot","text":"object class ggplot containing ridgeplot GSEA results.","code":""},{"path":"/reference/gseaRidgeplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create a ridgeplot to display the results of GSEA analysis — gseaRidgeplot","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/gseaRidgeplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a ridgeplot to display the results of GSEA analysis — gseaRidgeplot","text":"","code":"# load example FunctionalEnrichment object obj <- loadExamples(\"FunctionalEnrichment\")  # extract results res <- enrichmentResults(obj)  # plot results gseaRidgeplot(obj) #> Picking joint bandwidth of 0.561"},{"path":"/reference/integratedPathways.html","id":null,"dir":"Reference","previous_headings":"","what":"Access the results of integrative miRNA-mRNA pathway analyses — integratedPathways","title":"Access the results of integrative miRNA-mRNA pathway analyses — integratedPathways","text":"function accesses data slot FunctionalEnrichment object returns data.frame results integrative topological analysis carried topologicalAnalysis() function.","code":""},{"path":"/reference/integratedPathways.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access the results of integrative miRNA-mRNA pathway analyses — integratedPathways","text":"","code":"integratedPathways(object)"},{"path":"/reference/integratedPathways.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access the results of integrative miRNA-mRNA pathway analyses — integratedPathways","text":"object object class IntegrativePathwayAnalysis containing results miRNA-mRNA pathway analysis","code":""},{"path":"/reference/integratedPathways.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access the results of integrative miRNA-mRNA pathway analyses — integratedPathways","text":"data.frame object containing results topological analysis, returned topologicalAnalysis() function.","code":""},{"path":"/reference/integratedPathways.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Access the results of integrative miRNA-mRNA pathway analyses — integratedPathways","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/integratedPathways.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access the results of integrative miRNA-mRNA pathway analyses — integratedPathways","text":"","code":"# load the example IntegrativePathwayAnalysis object obj <- loadExamples(\"IntegrativePathwayAnalysis\")  # extract results taipaRes <- integratedPathways(obj)"},{"path":"/reference/integration.html","id":null,"dir":"Reference","previous_headings":"","what":"Explore the results of the integration analysis between miRNAs and genes — integration","title":"Explore the results of the integration analysis between miRNAs and genes — integration","text":"performing integration analysis miRNA gene expression values mirnaIntegration() function, results stored integration slot MirnaExperiment object can explored function.","code":""},{"path":"/reference/integration.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Explore the results of the integration analysis between miRNAs and genes — integration","text":"","code":"integration(object, param = FALSE)"},{"path":"/reference/integration.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Explore the results of the integration analysis between miRNAs and genes — integration","text":"object MirnaExperiment object containing miRNA gene data param Logical, whether return complete list object parameters used, just results stored data. Default FALSE","code":""},{"path":"/reference/integration.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Explore the results of the integration analysis between miRNAs and genes — integration","text":"param FALSE, functions returns data.frame object containing results integration analysis. Otherwise, list object including parameters used analysis returned.","code":""},{"path":"/reference/integration.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Explore the results of the integration analysis between miRNAs and genes — integration","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/integration.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Explore the results of the integration analysis between miRNAs and genes — integration","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # perform Kendall's correlation analysis with tau > 0.8 and p < 0.05 obj <- mirnaIntegration(obj,     test = \"correlation\",     corMethod = \"kendall\", corCutoff = 0.8 ) #> As specified by the user, a correlation will be used. #> Performing Kendall's correlation analysis... #> A statistically significant correlation between 1 miRNA-target pairs was found!  # visualize the results of correlation analysis res <- integration(obj) res #>                      microRNA Target microRNA.Direction Corr.Coefficient #> hsa.miR.21.5p.8 hsa-miR-21-5p  MATN2        upregulated       -0.8166667 #>                 Corr.P.Value Corr.Adjusted.P.Val #> hsa.miR.21.5p.8 5.116119e-06         0.003289664"},{"path":"/reference/integrationDatabase.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the database used for integrative miRNA-mRNA pathway analyses — integrationDatabase","title":"Extract the database used for integrative miRNA-mRNA pathway analyses — integrationDatabase","text":"function accesses database slot FunctionalEnrichment object returns name database used topologicalAnalysis() function perform integrative topological analysis.","code":""},{"path":"/reference/integrationDatabase.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the database used for integrative miRNA-mRNA pathway analyses — integrationDatabase","text":"","code":"integrationDatabase(object)"},{"path":"/reference/integrationDatabase.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the database used for integrative miRNA-mRNA pathway analyses — integrationDatabase","text":"object object class IntegrativePathwayAnalysis containing results miRNA-mRNA pathway analysis","code":""},{"path":"/reference/integrationDatabase.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the database used for integrative miRNA-mRNA pathway analyses — integrationDatabase","text":"character object name database used topologicalAnalysis() function, KEGG.","code":""},{"path":"/reference/integrationDatabase.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract the database used for integrative miRNA-mRNA pathway analyses — integrationDatabase","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/integrationDatabase.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the database used for integrative miRNA-mRNA pathway analyses — integrationDatabase","text":"","code":"# load the example IntegrativePathwayAnalysis object obj <- loadExamples(\"IntegrativePathwayAnalysis\")  # see the database integrationDatabase(obj) #> [1] \"KEGG\""},{"path":"/reference/integrationDotplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Display integrated miRNA-mRNA augmented pathways in a dotplot — integrationDotplot","title":"Display integrated miRNA-mRNA augmented pathways in a dotplot — integrationDotplot","text":"function produces dotplot depicts results topologically-aware integrative pathway analysis (TAIPA) carried topologicalAnalysis() function.","code":""},{"path":"/reference/integrationDotplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Display integrated miRNA-mRNA augmented pathways in a dotplot — integrationDotplot","text":"","code":"integrationDotplot(   object,   showTerms = 10,   showTermsParam = \"normalized.score\",   title = NULL )"},{"path":"/reference/integrationDotplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Display integrated miRNA-mRNA augmented pathways in a dotplot — integrationDotplot","text":"object object class IntegrativePathwayAnalysis showTerms number pathways shown, based order determined parameter showTermsParam; , alternatively, character vector indicating pathways plot. Default 10 showTermsParam order top pathways selected specified showTerms parameter. must one coverage, padj, pval, score normalized.score (default) title title plot. Default NULL include plot title","code":""},{"path":"/reference/integrationDotplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Display integrated miRNA-mRNA augmented pathways in a dotplot — integrationDotplot","text":"ggplot graph dotplot integrated pathways.","code":""},{"path":"/reference/integrationDotplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Display integrated miRNA-mRNA augmented pathways in a dotplot — integrationDotplot","text":"producing dotplot function, significant pathways ordered x-axis basis normalized pathway score computed topologicalAnalysis(). higher score, affected pathway biological conditions. Moreover, size dot equal ratio number nodes measurement available, total number nodes (pathway coverage). Finally, color scale dots relative adjusted p-values pathway.","code":""},{"path":"/reference/integrationDotplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Display integrated miRNA-mRNA augmented pathways in a dotplot — integrationDotplot","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/integrationDotplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Display integrated miRNA-mRNA augmented pathways in a dotplot — integrationDotplot","text":"","code":"# load example IntegrativePathwayAnalysis object obj <- loadExamples(\"IntegrativePathwayAnalysis\")  # access the results of pathway analysis integratedPathways(obj) #>                                                                                                 pathway #> Thyroid hormone synthesis                                                     Thyroid hormone synthesis #> Parathyroid hormone synthesis, secretion and action Parathyroid hormone synthesis, secretion and action #> Neurotrophin signaling pathway                                           Neurotrophin signaling pathway #> Cholinergic synapse                                                                 Cholinergic synapse #> GnRH signaling pathway                                                           GnRH signaling pathway #> Estrogen signaling pathway                                                   Estrogen signaling pathway #> Relaxin signaling pathway                                                     Relaxin signaling pathway #>                                                      coverage     score #> Thyroid hormone synthesis                           0.3469388 12.129412 #> Parathyroid hormone synthesis, secretion and action 0.2752294  6.082639 #> Neurotrophin signaling pathway                      0.2362205  5.003782 #> Cholinergic synapse                                 0.2019231  5.357688 #> GnRH signaling pathway                              0.1818182  5.831885 #> Estrogen signaling pathway                          0.2043796  5.117856 #> Relaxin signaling pathway                           0.2388060  5.113116 #>                                                     normalized.score #> Thyroid hormone synthesis                                   9.319461 #> Parathyroid hormone synthesis, secretion and action         4.787852 #> Neurotrophin signaling pathway                              3.123395 #> Cholinergic synapse                                         3.406131 #> GnRH signaling pathway                                      3.784109 #> Estrogen signaling pathway                                  3.450887 #> Relaxin signaling pathway                                   2.984415 #>                                                           P.Val   adj.P.Val #> Thyroid hormone synthesis                           0.000999001 0.000999001 #> Parathyroid hormone synthesis, secretion and action 0.000999001 0.000999001 #> Neurotrophin signaling pathway                      0.006993007 0.006993007 #> Cholinergic synapse                                 0.006993007 0.006993007 #> GnRH signaling pathway                              0.006993007 0.006993007 #> Estrogen signaling pathway                          0.006993007 0.006993007 #> Relaxin signaling pathway                           0.014985015 0.014985015  # create a dotplot of integrated pathways integrationDotplot(obj)"},{"path":"/reference/listPathways.html","id":null,"dir":"Reference","previous_headings":"","what":"List all the available biological pathways in KEGG, Reactome and WikiPathways — listPathways","title":"List all the available biological pathways in KEGG, Reactome and WikiPathways — listPathways","text":"function can used retrieve list valid biological pathways present KEGG, Reactome WikiPathways.","code":""},{"path":"/reference/listPathways.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"List all the available biological pathways in KEGG, Reactome and WikiPathways — listPathways","text":"","code":"listPathways(organism, database)"},{"path":"/reference/listPathways.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"List all the available biological pathways in KEGG, Reactome and WikiPathways — listPathways","text":"organism name organism consideration. different databases different supported organisms. see list supported organisms given database, use supportedOrganisms() function database name database use. must one : KEGG, Reactome, WikiPathways","code":""},{"path":"/reference/listPathways.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"List all the available biological pathways in KEGG, Reactome and WikiPathways — listPathways","text":"character vector containing pathway names present specified database.","code":""},{"path":"/reference/listPathways.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"List all the available biological pathways in KEGG, Reactome and WikiPathways — listPathways","text":"function uses graphite package retrieve biological pathways KEGG, Reactome WikiPathways.","code":""},{"path":"/reference/listPathways.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"List all the available biological pathways in KEGG, Reactome and WikiPathways — listPathways","text":"Sales, G., Calura, E., Cavalieri, D. et al. graphite - Bioconductor package convert pathway topology gene network. BMC Bioinformatics 13, 20 (2012), https://doi.org/10.1186/1471-2105-13-20.","code":""},{"path":"/reference/listPathways.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"List all the available biological pathways in KEGG, Reactome and WikiPathways — listPathways","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/listPathways.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"List all the available biological pathways in KEGG, Reactome and WikiPathways — listPathways","text":"","code":"# list the mouse pathways present in WikiPathways listPathways(\"Mus musculus\", \"WikiPathways\") #>   [1] \"Statin pathway\"                                                                   #>   [2] \"Cholesterol biosynthesis\"                                                         #>   [3] \"Selenium metabolism / selenoproteins\"                                             #>   [4] \"TGF-beta signaling pathway\"                                                       #>   [5] \"Hedgehog signaling pathway\"                                                       #>   [6] \"Glucuronidation\"                                                                  #>   [7] \"EBV LMP1 signaling\"                                                               #>   [8] \"Estrogen signaling\"                                                               #>   [9] \"Transcriptional activation by Nfe2l2 in response to phytochemicals\"               #>  [10] \"Methylation\"                                                                      #>  [11] \"EPO receptor signaling\"                                                           #>  [12] \"Amino acid conjugation of benzoic acid\"                                           #>  [13] \"Type II interferon signaling (IFNG)\"                                              #>  [14] \"Apoptosis\"                                                                        #>  [15] \"Nod-like receptor (NLR) signaling pathway\"                                        #>  [16] \"Retinol metabolism\"                                                               #>  [17] \"ErbB signaling pathway\"                                                           #>  [18] \"Aflatoxin B1 metabolism\"                                                          #>  [19] \"Mitochondrial gene expression\"                                                    #>  [20] \"Estrogen metabolism\"                                                              #>  [21] \"Polyol pathway\"                                                                   #>  [22] \"SIDS susceptibility pathways\"                                                     #>  [23] \"Endochondral ossification\"                                                        #>  [24] \"Selenium micronutrient network\"                                                   #>  [25] \"Folic acid network\"                                                               #>  [26] \"Oxidation by cytochrome P450\"                                                     #>  [27] \"Oxidative damage response\"                                                        #>  [28] \"Dopaminergic neurogenesis\"                                                        #>  [29] \"Regulation of cardiac hypertrophy by miR-208\"                                     #>  [30] \"MicroRNAs in cardiomyocyte hypertrophy\"                                           #>  [31] \"Glycolysis and gluconeogenesis\"                                                   #>  [32] \"Iron homeostasis\"                                                                 #>  [33] \"Cytoplasmic ribosomal proteins\"                                                   #>  [34] \"Glutathione metabolism\"                                                           #>  [35] \"Apoptosis modulation by HSP70\"                                                    #>  [36] \"Acetylcholine synthesis\"                                                          #>  [37] \"Mechanisms associated with pluripotency\"                                          #>  [38] \"One-carbon metabolism and related pathways\"                                       #>  [39] \"Kennedy pathway\"                                                                  #>  [40] \"Heme biosynthesis\"                                                                #>  [41] \"GPCRs, class A rhodopsin-like\"                                                    #>  [42] \"Splicing factor NOVA regulated synaptic proteins\"                                 #>  [43] \"Complement activation, classical pathway\"                                         #>  [44] \"Ptf1a related regulatory pathway\"                                                 #>  [45] \"Hypertrophy model\"                                                                #>  [46] \"Heart development\"                                                                #>  [47] \"Neural crest differentiation\"                                                     #>  [48] \"Alzheimer's disease\"                                                              #>  [49] \"Serotonin receptor 2 and STAT3 signaling\"                                         #>  [50] \"SREBF and miR33 in cholesterol and lipid homeostasis\"                             #>  [51] \"Serotonin and anxiety-related events\"                                             #>  [52] \"Serotonin and anxiety\"                                                            #>  [53] \"BDNF pathway\"                                                                     #>  [54] \"Purine metabolism\"                                                                #>  [55] \"Chemokine signaling pathway\"                                                      #>  [56] \"PPAR signaling pathway\"                                                           #>  [57] \"Fatty acid oxidation\"                                                             #>  [58] \"G protein signaling pathways\"                                                     #>  [59] \"miRNAs and TFs in iPS Cell Generation\"                                            #>  [60] \"Osteoblast signaling\"                                                             #>  [61] \"Spinal cord injury\"                                                               #>  [62] \"Mapk cascade\"                                                                     #>  [63] \"Primary focal segmental glomerulosclerosis (FSGS)\"                                #>  [64] \"Focal adhesion: PI3K-Akt-mTOR signaling pathway\"                                  #>  [65] \"Gene regulatory network modelling somitogenesis\"                                  #>  [66] \"White fat cell differentiation\"                                                   #>  [67] \"Notch signaling pathway\"                                                          #>  [68] \"Electron transport chain\"                                                         #>  [69] \"G13 signaling pathway\"                                                            #>  [70] \"Translation factors\"                                                              #>  [71] \"Glycogen metabolism\"                                                              #>  [72] \"Eicosanoid synthesis\"                                                             #>  [73] \"Fatty acid omega-oxidation\"                                                       #>  [74] \"ESC pluripotency pathways\"                                                        #>  [75] \"p38 Mapk signaling pathway\"                                                       #>  [76] \"ApoE and miR-146 in inflammation and atherosclerosis\"                             #>  [77] \"Tyrobp causal network in microglia\"                                               #>  [78] \"Microglia pathogen phagocytosis pathway\"                                          #>  [79] \"Lung fibrosis\"                                                                    #>  [80] \"Parkinson's disease\"                                                              #>  [81] \"EDA signaling in hair follicle development\"                                       #>  [82] \"Novel Jun-Dmp1 pathway\"                                                           #>  [83] \"BMP signaling pathway in eyelid development\"                                      #>  [84] \"Hfe effect on hepcidin production\"                                                #>  [85] \"Factors and pathways affecting insulin-like growth factor (IGF1)-Akt signaling\"   #>  [86] \"IL-1 signaling pathway\"                                                           #>  [87] \"Prostaglandin synthesis and regulation\"                                           #>  [88] \"Myometrial relaxation and contraction pathways\"                                   #>  [89] \"Wnt signaling in kidney disease\"                                                  #>  [90] \"Robo4 and VEGF signaling pathways crosstalk\"                                      #>  [91] \"ACE inhibitor pathway\"                                                            #>  [92] \"miR-127 in mesendoderm differentiation\"                                           #>  [93] \"Wnt signaling pathway\"                                                            #>  [94] \"Oxidative stress response\"                                                        #>  [95] \"G1 to S cell cycle control\"                                                       #>  [96] \"Distal convoluted tubule 1 (DCT1) cell\"                                           #>  [97] \"Ethanol metabolism resulting in production of ROS by CYP2E1\"                      #>  [98] \"Nuclear receptors in lipid metabolism and toxicity\"                               #>  [99] \"Eicosanoid lipid synthesis map\"                                                   #> [100] \"TCA cycle\"                                                                        #> [101] \"Sphingolipid metabolism overview\"                                                 #> [102] \"Glycerolipids and glycerophospholipids\"                                           #> [103] \"Cholesterol metabolism with Bloch and Kandutsch-Russell pathways\"                 #> [104] \"Eicosanoid metabolism via cyclooxygenases (COX)\"                                  #> [105] \"Eicosanoid metabolism via lipoxygenases (LOX)\"                                    #> [106] \"Eicosanoid metabolism via cytochrome P450 monooxygenases\"                         #> [107] \"One-carbon metabolism\"                                                            #> [108] \"Omega-3 / omega-6 fatty acid synthesis\"                                           #> [109] \"Omega-9 fatty acid synthesis\"                                                     #> [110] \"Oxidative stress and redox pathway\"                                               #> [111] \"Circulating monocytes and cardiac macrophages in diastolic dysfunction\"           #> [112] \"Elongation of (very) long chain fatty acids\"                                      #> [113] \"Osteoclast signaling\"                                                             #> [114] \"Inflammatory response pathway\"                                                    #> [115] \"Blood clotting cascade\"                                                           #> [116] \"Lipids measured in liver metastasis from breast cancer\"                           #> [117] \"Sphingolipid metabolism (integrated pathway)\"                                     #> [118] \"Regulation of Pgc1a expression by a Gsk3b-Tfeb signaling axis in skeletal muscle\" #> [119] \"GDNF/RET signaling axis\"                                                          #> [120] \"Peroxiredoxin 2 induced ovarian failure\"                                          #> [121] \"Mapk signaling pathway\"                                                           #> [122] \"Deregulation of renin-angiotensin system by SARS-CoV infection\"                   #> [123] \"Hypoxia-dependent self-renewal of myoblasts\"                                      #> [124] \"Hypoxia-dependent proliferation of myoblasts\"                                     #> [125] \"Hypoxia-dependent differentiation of myoblasts\"                                   #> [126] \"Burn wound healing\"                                                               #> [127] \"Fibrin complement receptor 3 signaling pathway\"                                   #> [128] \"Proteasome degradation\"                                                           #> [129] \"Biogenic amine synthesis\"                                                         #> [130] \"Regulation of actin cytoskeleton\"                                                 #> [131] \"Lac-Phe pathway\"                                                                  #> [132] \"Comprehensive IL-17A signaling\"                                                   #> [133] \"Dravet syndrome: Scn1a-A1783V point mutation model\"                               #> [134] \"Globo series sphingolipid metabolism\"                                             #> [135] \"Synthesis and degradation of ketone bodies\"                                       #> [136] \"Exercise-induced circadian regulation\"                                            #> [137] \"Steroid biosynthesis\"                                                             #> [138] \"Calcium regulation in cardiac cells\"                                              #> [139] \"Signal transduction of S1P receptor\"                                              #> [140] \"FAS pathway and stress induction of HSP regulation\"                               #> [141] \"Leptin-insulin signaling overlap\"                                                 #> [142] \"Integrin-mediated cell adhesion\"                                                  #> [143] \"miR-1 in cardiac development\"                                                     #> [144] \"Pentose phosphate pathway\"                                                        #> [145] \"Insulin signaling\"                                                                #> [146] \"Amino acid metabolism\"                                                            #> [147] \"Leptin and adiponectin\"                                                           #> [148] \"Wnt signaling pathway and pluripotency\"                                           #> [149] \"Glutathione and one-carbon metabolism\"                                            #> [150] \"Focal adhesion\"                                                                   #> [151] \"Toll-like receptor signaling\"                                                     #> [152] \"Oxidative phosphorylation\"                                                        #> [153] \"Arachidonate epoxygenase / epoxide hydrolase\"                                     #> [154] \"Metapathway biotransformation\"                                                    #> [155] \"Fatty acid beta-oxidation\"                                                        #> [156] \"Fatty acid biosynthesis\"                                                          #> [157] \"Tryptophan metabolism\"                                                            #> [158] \"Nucleotide GPCRs\"                                                                 #> [159] \"GPCRs, small ligand\"                                                              #> [160] \"Monoamine GPCRs\""},{"path":"/reference/loadExamples.html","id":null,"dir":"Reference","previous_headings":"","what":"Load example MIRit objects — loadExamples","title":"Load example MIRit objects — loadExamples","text":"helper function allows create MirnaExperiment object containing miRNA gene expression data deriving Riesco-Eizaguirre et al (2015), IntegrativePathwayAnalysis object containing TAIPA results dataset, FunctionalEnrichment example GSEA enrichment results.","code":""},{"path":"/reference/loadExamples.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Load example MIRit objects — loadExamples","text":"","code":"loadExamples(class = \"MirnaExperiment\")"},{"path":"/reference/loadExamples.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Load example MIRit objects — loadExamples","text":"class must MirnaExperiment (default) load example object class MirnaExperiment, IntegrativePathwayAnalysis, load example object class IntegrativePathwayAnalysis, FunctionalEnrichment, load example object class FunctionalEnrichment.","code":""},{"path":"/reference/loadExamples.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Load example MIRit objects — loadExamples","text":"example MirnaExperiment object, IntegrativePathwayAnalysis object, FunctionalEnrichment object.","code":""},{"path":"/reference/loadExamples.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Load example MIRit objects — loadExamples","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/loadExamples.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Load example MIRit objects — loadExamples","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # load example IntegrativePathwayAnalysis object obj <- loadExamples(\"IntegrativePathwayAnalysis\")  # load example FunctionalEnrichment object obj <- loadExamples(\"FunctionalEnrichment\")"},{"path":"/reference/mirVariantPlot.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a trackplot to show the association between miRNAs and disease-SNPs — mirVariantPlot","title":"Create a trackplot to show the association between miRNAs and disease-SNPs — mirVariantPlot","text":"function plots trackplot shows genomic position disease-associated SNPs affect miRNA genes. useful visualize genomic position context disease-associated variants may affect miRNA expression.","code":""},{"path":"/reference/mirVariantPlot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a trackplot to show the association between miRNAs and disease-SNPs — mirVariantPlot","text":"","code":"mirVariantPlot(   variantId,   snpAssociation,   showContext = FALSE,   showSequence = TRUE,   snpFill = \"lightblue\",   mirFill = \"orange\",   from = NULL,   to = NULL,   title = NULL,   ... )"},{"path":"/reference/mirVariantPlot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a trackplot to show the association between miRNAs and disease-SNPs — mirVariantPlot","text":"variantId valid name SNP variant! (e.g. \"rs394581\") snpAssociation data.frame object containing results findMirnaSNPs() function showContext Logical, TRUE complete genomic context genes present region shown. Default FALSE just display variant miRNA gene showSequence Logical, whether display color-coded sequence bottom trackplot. Default TRUE. parameter set FALSE showContext TRUE snpFill must R color name specifies fill color SNP locus. Default lightblue. Available color formats include color names, 'blue' 'red', hexadecimal colors specified #RRGGBB mirFill must R color name specifies fill color miRNA locus. Default orange. Available color formats include color names, 'blue' 'red', hexadecimal colors specified #RRGGBB start position plotted genomic range. Default NULL automatically determine appropriate position end position plotted genomic range. Default NULL automatically determine appropriate position title title plot. Default NULL include plot title ... parameters can passed Gviz::plotTracks() function","code":""},{"path":"/reference/mirVariantPlot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a trackplot to show the association between miRNAs and disease-SNPs — mirVariantPlot","text":"trackplot information chromosome, SNP miRNA gene location.","code":""},{"path":"/reference/mirVariantPlot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Create a trackplot to show the association between miRNAs and disease-SNPs — mirVariantPlot","text":"function retrieves genomic coordinates output findMirnaSNPs() function uses Gviz package build trackplot.","code":""},{"path":"/reference/mirVariantPlot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Create a trackplot to show the association between miRNAs and disease-SNPs — mirVariantPlot","text":"Hahne, F., Ivanek, R. (2016). Visualizing Genomic Data Using Gviz Bioconductor. : Mathé, E., Davis, S. (eds) Statistical Genomics. Methods Molecular Biology, vol 1418. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3578-9_16","code":""},{"path":"/reference/mirVariantPlot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create a trackplot to show the association between miRNAs and disease-SNPs — mirVariantPlot","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/mirVariantPlot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a trackplot to show the association between miRNAs and disease-SNPs — mirVariantPlot","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  if (FALSE) { # retrieve associated SNPs association <- findMirnaSNPs(obj, disId)  # visualize association as a trackplot mirVariantPlot(variantId = varId, snpAssociation = association) }"},{"path":"/reference/mirnaCounts.html","id":null,"dir":"Reference","previous_headings":"","what":"Count matrix for microRNA expression in thyroid cancer — mirnaCounts","title":"Count matrix for microRNA expression in thyroid cancer — mirnaCounts","text":"dataset contains gene expression matrix resulting miRNA-Seq analysis thyroid cancer. Specifically, data originate Riesco-Eizaguirre et al (2015), sequenced 8 papillary thyroid carcinomas (PTC) together paired samples normal thyroid tissue. thing done mRNAs order investigate effects target genes. Data included package obtained Gene Expression Omnibus (GEO accession: GSE63511).","code":""},{"path":"/reference/mirnaCounts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count matrix for microRNA expression in thyroid cancer — mirnaCounts","text":"","code":"data(mirnaCounts)"},{"path":[]},{"path":"/reference/mirnaCounts.html","id":"mirnacounts","dir":"Reference","previous_headings":"","what":"mirnaCounts","title":"Count matrix for microRNA expression in thyroid cancer — mirnaCounts","text":"matrix object containing samples columns microRNAs rows.","code":""},{"path":"/reference/mirnaCounts.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Count matrix for microRNA expression in thyroid cancer — mirnaCounts","text":"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63511","code":""},{"path":"/reference/mirnaCounts.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Count matrix for microRNA expression in thyroid cancer — mirnaCounts","text":"Garcilaso Riesco-Eizaguirre et al., “MiR-146b-3p/PAX8/NIS Regulatory Circuit Modulates Differentiation Phenotype Function Thyroid Cells Carcinogenesis,” Cancer Research 75, . 19 (September 30, 2015): 4119–30, https://doi.org/10.1158/0008-5472.CAN-14-3547.","code":""},{"path":"/reference/mirnaIntegration.html","id":null,"dir":"Reference","previous_headings":"","what":"Integrate microRNA and gene expression — mirnaIntegration","title":"Integrate microRNA and gene expression — mirnaIntegration","text":"function allows identify microRNAs significantly associated/correlated targets. principle , since biological role miRNAs mainly negatively regulate gene expression post-transcriptionally, expression microRNA negatively correlated expression targets. test assumption matched-sample data, function performs correlation analysis. hand, unpaired data, offers different one-sided association tests estimate targets -regulated miRNAs enriched -regulated genes vice versa. Additionally, unpaired data, miRNA effects target gene expression can also quantified fast approximation rotation gene-set testing ('fry' method). correlation analyses, default behavior use Spearman's correlation analysis, whereas association tests default option makes use one-sided Boschloo's exact test. See details section information.","code":""},{"path":"/reference/mirnaIntegration.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Integrate microRNA and gene expression — mirnaIntegration","text":"","code":"mirnaIntegration(   mirnaObj,   test = \"auto\",   pCutoff = 0.05,   pAdjustment = \"fdr\",   corMethod = \"spearman\",   corCutoff = 0.5,   associationMethod = \"boschloo\",   nuisanceParam = 100 )"},{"path":"/reference/mirnaIntegration.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Integrate microRNA and gene expression — mirnaIntegration","text":"mirnaObj MirnaExperiment object containing miRNA gene data test statistical test evaluate association miRNAs genes. must one auto (default), automatically determine appropriate statistical test; correlation, perform correlation analysis; association, perform one-sided association test; fry perform integrative analysis rotation gene-set testing pCutoff adjusted p-value cutoff use statistical significance. default value 0.05 pAdjustment p-value correction method multiple testing. must one : fdr (default), BH, none, holm, hochberg, hommel, bonferroni, corMethod correlation method used correlation analysis. must one : spearman (default), pearson, kendall. See details section information corCutoff minimum (negative) value correlation coefficient consider meaningful miRNA-target relationship. Default 0.5 associationMethod statistical test used evaluating association miRNAs targets unpaired data. must one boschloo (default), perform one-sided Boschloo's exact test; fisher-midp, compute one-sided Fisher's exact test Lancaster's mid-p correction; fisher, perform one-sided Fisher's exact test nuisanceParam number nuisance parameter values considered p-value calculation boschloo method. higher value, better p-value estimation accuracy. Default 100","code":""},{"path":"/reference/mirnaIntegration.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Integrate microRNA and gene expression — mirnaIntegration","text":"MirnaExperiment object containing integration results. access results, user can make use integration() function. additional details interpret results miRNA-gene integrative analysis, please see MirnaExperiment.","code":""},{"path":"/reference/mirnaIntegration.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Integrate microRNA and gene expression — mirnaIntegration","text":"already pointed , miRNA gene expression data derive samples, correlation analysis used. evaluating relationships, default method used Spearman's correlation coefficient, : need normally distributed data; assume linearity; much resistant outliers. However, user can also decide use correlation methods, Pearson's Kendall's correlation. Nevertheless, NGS data may happen certain number ties present expression values. can handled spearman method computes tie-corrected version Spearman's coefficients. However, another correlation method suitable perform rank correlation tied data Kendall's tau-b method, usable kendall. Regarding correlation direction, since miRNAs mainly act negative regulators, negatively correlated miRNA-target pairs evaluated, statistical significance calculated one-tailed t-test. Please notice strong batch effects noticed expression data, recommended remove batchCorrection() function implemented MIRit. Moreover, gene expression data miRNA expression data derive different samples (unpaired data), correlation analysis performed. However, one-sided association tests can applied cases evaluate targets -regulated miRNAs statistically enriched -regulated genes, , conversely, targets -regulated miRNAs statistically enriched -regulated genes. case, Fisher's exact test can used assess statistical significance inverse association. Moreover, Lancaster's mid-p adjustment can applied since shown increases statistical power retaining Type error rates. However, Fisher's exact test conditional test requires sum rows columns contingency table fixed. Notably, true genomic data likely different datasets may lead different number DEGs. Therefore, default behavior MIRit use variant Barnard's exact test, named Boschloo's exact test, suitable group sizes contingency tables variable. Moreover, possible demonstrate Boschloo's test uniformly powerful compared Fisher's exact test. Finally, unpaired data, effect DE-miRNAs expression target genes can estimated rotation gene-set tests. particular, fast approximation rotation gene-set testing called fry, implemented limma package, can used statistically quantify influence miRNAs expression changes target genes.","code":""},{"path":"/reference/mirnaIntegration.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Integrate microRNA and gene expression — mirnaIntegration","text":"Ritchie , Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015). “limma powers differential expression analyses RNA-sequencing microarray studies.” Nucleic Acids Research, 43(7), e47. doi:10.1093/nar/gkv007. Di Wu others, ROAST: rotation gene set tests complex microarray experiments, Bioinformatics, Volume 26, Issue 17, September 2010, Pages 2176–2182, https://doi.org/10.1093/bioinformatics/btq401. Routledge, R. D. (1994). Practicing Safe Statistics Mid-p. Canadian Journal Statistics / La Revue Canadienne de Statistique, 22(1), 103–110, https://doi.org/10.2307/3315826. Boschloo R.D. (1970). \"Raised Conditional Level Significance 2x2-table Testing Equality Two Probabilities\". Statistica Neerlandica. 24: 1–35. doi:10.1111/j.1467-9574.1970.tb00104.x.","code":""},{"path":"/reference/mirnaIntegration.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Integrate microRNA and gene expression — mirnaIntegration","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/mirnaIntegration.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Integrate microRNA and gene expression — mirnaIntegration","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # perform integration analysis with default settings obj <- mirnaIntegration(obj) #> Since data derive from paired samples, a correlation test will be used. #> Performing Spearman's correlation analysis... #> A statistically significant correlation between 215 miRNA-target pairs was found!  # perform Kendall's correlation analysis with tau > 0.8 and p < 0.05 obj <- mirnaIntegration(obj,     test = \"correlation\",     corMethod = \"kendall\", corCutoff = 0.8 ) #> As specified by the user, a correlation will be used. #> Performing Kendall's correlation analysis... #> A statistically significant correlation between 1 miRNA-target pairs was found!"},{"path":"/reference/mirnaTargets.html","id":null,"dir":"Reference","previous_headings":"","what":"Explore miRNA-target pairs — mirnaTargets","title":"Explore miRNA-target pairs — mirnaTargets","text":"function accesses targets slot MirnaExperiment object. retrieving miRNA targets getTargets() function, interactions miRNAs target genes stored targets slot can explored function.","code":""},{"path":"/reference/mirnaTargets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Explore miRNA-target pairs — mirnaTargets","text":"","code":"mirnaTargets(object)"},{"path":"/reference/mirnaTargets.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Explore miRNA-target pairs — mirnaTargets","text":"object MirnaExperiment object containing miRNA gene data","code":""},{"path":"/reference/mirnaTargets.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Explore miRNA-target pairs — mirnaTargets","text":"data.frame object containing interactions miRNAs target genes, retrieved getTargets() function.","code":""},{"path":"/reference/mirnaTargets.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Explore miRNA-target pairs — mirnaTargets","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/mirnaTargets.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Explore miRNA-target pairs — mirnaTargets","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # visualize targets targets_df <- mirnaTargets(obj)"},{"path":"/reference/pairedSamples.html","id":null,"dir":"Reference","previous_headings":"","what":"View the relationship between miRNA and gene samples — pairedSamples","title":"View the relationship between miRNA and gene samples — pairedSamples","text":"function allows access pairedSamples slot MirnaExperiment object. MirnaExperiment class able contain miRNA gene expression measurements deriving individuals (paired samples), different subjects (unpaired samples).","code":""},{"path":"/reference/pairedSamples.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"View the relationship between miRNA and gene samples — pairedSamples","text":"","code":"pairedSamples(object)"},{"path":"/reference/pairedSamples.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"View the relationship between miRNA and gene samples — pairedSamples","text":"object MirnaExperiment object containing miRNA gene data","code":""},{"path":"/reference/pairedSamples.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"View the relationship between miRNA and gene samples — pairedSamples","text":"logical value either TRUE paired samples, FALSE unpaired samples.","code":""},{"path":"/reference/pairedSamples.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"View the relationship between miRNA and gene samples — pairedSamples","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/pairedSamples.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"View the relationship between miRNA and gene samples — pairedSamples","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # check if an existing MirnaExperiment object derive from paired samples pairedSamples(obj) #> [1] TRUE"},{"path":"/reference/plotCorrelation.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot correlation between miRNAs and genes within biological groups — plotCorrelation","title":"Plot correlation between miRNAs and genes within biological groups — plotCorrelation","text":"function creates scatter plot shows correlation miRNA gene expression levels. useful correlation analysis performed mirnaIntegration() function, graphically visualize quantitative effect miRNA dysregulations target gene expression. Furthermore, function performs linear/monotonic regression better represent relationships miRNA-target pairs.","code":""},{"path":"/reference/plotCorrelation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot correlation between miRNAs and genes within biological groups — plotCorrelation","text":"","code":"plotCorrelation(   mirnaObj,   mirna,   gene,   condition = NULL,   showCoeff = TRUE,   regression = TRUE,   useRanks = FALSE,   lineCol = \"red\",   lineType = \"dashed\",   lineWidth = 0.8,   pointSize = 3,   colorScale = NULL,   fontSize = 12,   fontFamily = \"\",   legend = \"top\",   borderWidth = 1,   allBorders = TRUE,   grid = TRUE )"},{"path":"/reference/plotCorrelation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot correlation between miRNAs and genes within biological groups — plotCorrelation","text":"mirnaObj MirnaExperiment object containing miRNA gene data mirna name miRNA want observe correlation gene name gene want observe correlation condition must NULL (default) plot expression based group variable used differential expression analysis. Alternatively, must character/factor object specifies group memberships (eg. c(\"healthy, \"healthy\", \"disease\", \"disease\")) showCoeff Logical, whether show correlation coeffficient . Note \"R\" used Pearson's correlation\", \"rho\" Spearman's correlation, \"tau\" Kendall's correlation. Default TRUE regression Logical, whether display linear/monotonic regression line fits miRNA-gene correlation data. Default TRUE useRanks Logical, whether represent non-parametric correlation analyses (Spearman's Kendall's correlations) rank-transformed data. Note case, linear regression performed ranked data instead monotonic regression. Default FALSE lineCol must R color name specifies color regression line. Default red. Available color formats include color names, 'blue' 'red', hexadecimal colors specified #RRGGBB lineType specifies line type used regression line. must either 'blank', 'solid', 'dashed' (default), 'dotted', 'dotdash', 'longdash' 'twodash' lineWidth width fitted regression line (default 0.8) pointSize size points correlation plot (default 3) colorScale must named character vector values correspond R colors, names coincide groups specified condition parameter (eg. c(\"healthy\" = \"green\", \"disease\" = \"red\")). Default NULL, order use default color scale. Available color formats include color names, 'blue' 'red', hexadecimal colors specified #RRGGBB fontSize base size text elements within plot. Default 12 fontFamily base family text elements within plot legend position legend. Allowed values top, bottom, right, left none. default setting top show legend plot. none specified, legend included graph. borderWidth width plot borders (default 1) allBorders Logical, whetether show panel borders, just bottom left borders. Default TRUE grid Logical, whether show grid lines . Default TRUE","code":""},{"path":"/reference/plotCorrelation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot correlation between miRNAs and genes within biological groups — plotCorrelation","text":"object class ggplot containing correlation scatter plot.","code":""},{"path":"/reference/plotCorrelation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot correlation between miRNAs and genes within biological groups — plotCorrelation","text":"non-parametric correlation performed mirnaIntegration() function, regression line can fitted monotonic regression expression levels, linear regression performed rank-transformed data. Since, ranks correspond real expression values, default option perform monotonic regression fit monotonic curve. , function makes use MonoPoly R package, implements algorithm proposed Murray et al. 2016.","code":""},{"path":"/reference/plotCorrelation.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Plot correlation between miRNAs and genes within biological groups — plotCorrelation","text":"K. Murray, S. Müller & B. . Turlach (2016) Fast flexible methods monotone polynomial fitting, Journal Statistical Computation Simulation, 86:15, 2946-2966, DOI: 10.1080/00949655.2016.1139582.","code":""},{"path":"/reference/plotCorrelation.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot correlation between miRNAs and genes within biological groups — plotCorrelation","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/plotCorrelation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot correlation between miRNAs and genes within biological groups — plotCorrelation","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # perform miRNA-target integration obj <- mirnaIntegration(obj) #> Since data derive from paired samples, a correlation test will be used. #> Performing Spearman's correlation analysis... #> A statistically significant correlation between 215 miRNA-target pairs was found!  # plot correlation between miR-146b and PAX8 with monotonic regression curve plotCorrelation(obj, \"hsa-miR-146b-5p\", \"PAX8\", condition = \"disease\")"},{"path":"/reference/plotDE.html","id":null,"dir":"Reference","previous_headings":"","what":"Represent differentially expressed miRNAs/genes as boxplots, barplots or violinplots — plotDE","title":"Represent differentially expressed miRNAs/genes as boxplots, barplots or violinplots — plotDE","text":"function able produce boxplots, barplots violinplots useful visualize miRNA gene differential expression. user just provide vector interesting miRNA/genes wants plot (e.g. \"hsa-miR-34a-5p\", \"hsa-miR-146b-5p\", \"PAX8\"). chart type can specified graph parameter.","code":""},{"path":"/reference/plotDE.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Represent differentially expressed miRNAs/genes as boxplots, barplots or violinplots — plotDE","text":"","code":"plotDE(   mirnaObj,   features,   condition = NULL,   graph = \"boxplot\",   linear = TRUE,   showSignificance = TRUE,   starSig = TRUE,   pCol = \"adj.P.Val\",   sigLabelSize = 7,   digits = 3,   nameAsTitle = FALSE,   colorScale = NULL,   fontSize = 12,   fontFamily = \"\",   legend = \"top\",   borderWidth = 1,   allBorders = FALSE,   grid = FALSE )"},{"path":"/reference/plotDE.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Represent differentially expressed miRNAs/genes as boxplots, barplots or violinplots — plotDE","text":"mirnaObj MirnaExperiment object containing miRNA gene data features character vector containing genes/miRNAs plot condition must NULL (default) plot expression based group variable used differential expression analysis. Alternatively, must character/factor object specifies group memberships (eg. c(\"healthy, \"healthy\", \"disease\", \"disease\")) graph type plot produce. must one boxplot (default), barplot, violinplot linear Logical, whether plot expression levels linear scale log2 space. Default TRUE order use linear space showSignificance Logical, whether display statistical significance . Default TRUE starSig Logical, whether represent statistical significance stars. Default TRUE, significance scale : * \\(p < 0.05\\), ** \\(p < 0.01\\), *** \\(p < 0.001\\), **** \\(p < 0.0001\\). starSig set FALSE, p-values adjusted p-values reported plot numbers pCol statistics used evaluate comparison significance. must one P.Value, use unadjusted p-values, adj.P.Val (default), use p-values corrected multiple testing sigLabelSize size labels used show statistical significance. Default 7, well suited representing p-values significance stars. However, starSig set FALSE, user might downsize parameter digits number digits show p-values reported numbers (starSig FALSE). Default 3 nameAsTitle Logical, set TRUE, miRNA/gene name added plot title, x-axis legend removed. Note option considered features contains just one miRNA/gene. Default FALSE colorScale must named character vector values correspond R colors, names coincide groups specified condition parameter (eg. c(\"healthy\" = \"green\", \"disease\" = \"red\")). Default NULL, order use default color scale. Available color formats include color names, 'blue' 'red', hexadecimal colors specified #RRGGBB fontSize base size text elements within plot. Default 12 fontFamily base family text elements within plot legend position legend. Allowed values top, bottom, right, left none. default setting top show legend plot. none specified, legend included graph. borderWidth width plot borders (default 1) allBorders Logical, whetether show panel borders, just bottom left borders. Default FALSE grid Logical, whether show grid lines . Default FALSE","code":""},{"path":"/reference/plotDE.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Represent differentially expressed miRNAs/genes as boxplots, barplots or violinplots — plotDE","text":"object class ggplot containing plot.","code":""},{"path":"/reference/plotDE.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Represent differentially expressed miRNAs/genes as boxplots, barplots or violinplots — plotDE","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/plotDE.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Represent differentially expressed miRNAs/genes as boxplots, barplots or violinplots — plotDE","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # produce a boxplot for PAX8 and miR-34a-5p plotDE(obj, features = c(\"hsa-miR-34a-5p\", \"PAX8\"))   # produce a barplot for PAX8 and miR-34a-5p without significance plotDE(obj,     features = c(\"hsa-miR-34a-5p\", \"PAX8\"),     graph = \"barplot\", showSignificance = FALSE ) #> Warning: Computation failed in `stat_summary()` #> Caused by error in `get()`: #> ! object 'mean_sd' of mode 'function' was not found   # produce a violinplot for BCL2 plotDE(obj, features = \"BCL2\", graph = \"violinplot\") #> Warning: Removed 77 rows containing missing values (`geom_violin()`)."},{"path":"/reference/plotDimensions.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate multidimensional scaling (MDS) plots to explore miRNA/gene expression distances — plotDimensions","title":"Generate multidimensional scaling (MDS) plots to explore miRNA/gene expression distances — plotDimensions","text":"function performs multidimensional scaling order produce simple scatterplot shows miRNA/gene expression variations among samples. particular, starting MirnaExperiment object, functions allows visualize miRNA gene expression multidimensional space. Moreover, possible color samples basis specific variables, extremely useful assess miRNA/gene expression variations distinct biological groups.","code":""},{"path":"/reference/plotDimensions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate multidimensional scaling (MDS) plots to explore miRNA/gene expression distances — plotDimensions","text":"","code":"plotDimensions(   mirnaObj,   assay,   condition = NULL,   dimensions = c(1, 2),   labels = FALSE,   boxedLabel = TRUE,   pointSize = 3,   pointAlpha = 1,   colorScale = NULL,   title = NULL,   fontSize = 12,   fontFamily = \"\",   legend = \"top\",   borderWidth = 1,   allBorders = TRUE,   grid = FALSE,   ... )"},{"path":"/reference/plotDimensions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate multidimensional scaling (MDS) plots to explore miRNA/gene expression distances — plotDimensions","text":"mirnaObj MirnaExperiment object containing miRNA gene data assay results display. must either 'microRNA', plot miRNA expression, 'genes', produce MDS plot genes condition must column name variable specified metadata (colData) MirnaExperiment object; , alternatively, must character/factor object specifies group memberships (eg. c(\"healthy, \"healthy\", \"disease\", \"disease\")) dimensions numeric vector length 2 indicates two dimensions represent plot. Default c(1, 2) plot two dimensions account highest portion variability labels Logical, whether display labels . Default FALSE boxedLabel Logical, whether show labels inside rectangular shape (default) just text elements pointSize size points MDS plot (default 3) pointAlpha transparency points MDS plot (default 1) colorScale must named character vector values correspond R colors, names coincide groups specified condition parameter (eg. c(\"healthy\" = \"green\", \"disease\" = \"red\")). Default NULL, order use default color scale. Available color formats include color names, 'blue' 'red', hexadecimal colors specified #RRGGBB title title plot. Default NULL include plot title fontSize base size text elements within plot. Default 12 fontFamily base family text elements within plot legend position legend. Allowed values top, bottom, right, left none. default setting top show legend plot. none specified, legend included graph. borderWidth width plot borders (default 1) allBorders Logical, whetether show panel borders, just bottom left borders. Default TRUE grid Logical, whether show grid lines . Default FALSE ... parameters can passed limma::plotMDS() function","code":""},{"path":"/reference/plotDimensions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate multidimensional scaling (MDS) plots to explore miRNA/gene expression distances — plotDimensions","text":"object class ggplot containing plot.","code":""},{"path":"/reference/plotDimensions.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Generate multidimensional scaling (MDS) plots to explore miRNA/gene expression distances — plotDimensions","text":"perform multidimensional scaling, function internally uses limma::plotMDS() function provided limma package.","code":""},{"path":"/reference/plotDimensions.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generate multidimensional scaling (MDS) plots to explore miRNA/gene expression distances — plotDimensions","text":"Ritchie , Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015). “limma powers differential expression analyses RNA-sequencing microarray studies.” Nucleic Acids Research, 43(7), e47. doi:10.1093/nar/gkv007.","code":""},{"path":"/reference/plotDimensions.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate multidimensional scaling (MDS) plots to explore miRNA/gene expression distances — plotDimensions","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/plotDimensions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate multidimensional scaling (MDS) plots to explore miRNA/gene expression distances — plotDimensions","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # produce MDS plot for miRNA expression with labels plotDimensions(obj, \"microRNA\", condition = \"disease\", labels = TRUE)   # produce MDS plot for genes without condition color plotDimensions(obj, \"genes\")"},{"path":"/reference/plotVolcano.html","id":null,"dir":"Reference","previous_headings":"","what":"Produce volcano plots to display miRNA/gene differential expression — plotVolcano","title":"Produce volcano plots to display miRNA/gene differential expression — plotVolcano","text":"function allows user create publication-quality volcano plots represent results miRNA/gene differential expression. kind plots, x-axis relative log2 fold change biological conditions, y-axis contains negative base-10 logarithm p-value. Note, even volcano plots display unadjusted p-values y-axis, cutoff level shown plot derive adjusted p-value cutoff used differential expression analysis.","code":""},{"path":"/reference/plotVolcano.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Produce volcano plots to display miRNA/gene differential expression — plotVolcano","text":"","code":"plotVolcano(   mirnaObj,   assay,   labels = NULL,   boxedLabel = TRUE,   pointSize = 3,   pointAlpha = 0.4,   interceptWidth = 0.6,   interceptColor = \"black\",   interceptType = \"dashed\",   colorScale = c(\"blue\", \"grey\", \"red\"),   title = NULL,   fontSize = 12,   fontFamily = \"\",   legend = \"none\",   borderWidth = 1,   allBorders = TRUE,   grid = FALSE )"},{"path":"/reference/plotVolcano.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Produce volcano plots to display miRNA/gene differential expression — plotVolcano","text":"mirnaObj MirnaExperiment object containing miRNA gene data assay results display. must either 'microRNA', plot miRNA differential expression, 'genes', show results genes labels labels show graph. Default NULL include labels. parameter can character vector containing IDs features want display. Alternatively, parameter can also number significant features want plot labels boxedLabel Logical, whether show labels inside rectangular shape (default) just text elements pointSize size points volcano plot (default 3) pointAlpha transparency points volcano plot (default 0.4) interceptWidth width cutoff intercepts (default 0.6) interceptColor must R color name specifies color cutoff intercepts. Default black. Available color formats include color names, 'blue' 'red', hexadecimal colors specified #RRGGBB interceptType specifies line type used cutoff intercepts. must either 'blank', 'solid', 'dashed' (default), 'dotted', 'dotdash', 'longdash' 'twodash' colorScale must character vector length 3 containing valid R color names downregulated, non significant, upregulated features, respectively. Default value c('blue', 'grey', 'red'). Available color formats include color names, 'blue' 'red', hexadecimal colors specified #RRGGBB title title plot. Default NULL include plot title fontSize base size text elements within plot. Default 12 fontFamily base family text elements within plot legend position legend. Allowed values top, bottom, right, left none. default setting none legend included graph. borderWidth width plot borders (default 1) allBorders Logical, whetether show panel borders, just bottom left borders. Default TRUE grid Logical, whether show grid lines . Default FALSE","code":""},{"path":"/reference/plotVolcano.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Produce volcano plots to display miRNA/gene differential expression — plotVolcano","text":"object class ggplot containing plot.","code":""},{"path":"/reference/plotVolcano.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Produce volcano plots to display miRNA/gene differential expression — plotVolcano","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/plotVolcano.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Produce volcano plots to display miRNA/gene differential expression — plotVolcano","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # produce a volcano plot for miRNAs with labels plotVolcano(obj, \"microRNA\", labels = 5)   # produce a volcano plot for genes plotVolcano(obj, \"genes\")"},{"path":"/reference/preparePathways.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare miRNA-augmented pathways for integrative miRNA-mRNA pathway analyses — preparePathways","title":"Prepare miRNA-augmented pathways for integrative miRNA-mRNA pathway analyses — preparePathways","text":"function takes influential miRNA-mRNA interactions, identified mirnaIntegration() function, adds biological pathways retrieved pathway database KEGG, WikiPathways Reactome. pathways returned function needed perform topologically-aware integrative pathway analysis (TAIPA) topologicalAnalysis() function.","code":""},{"path":"/reference/preparePathways.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare miRNA-augmented pathways for integrative miRNA-mRNA pathway analyses — preparePathways","text":"","code":"preparePathways(   mirnaObj,   database = \"KEGG\",   organism = \"Homo sapiens\",   minPc = 10,   BPPARAM = bpparam() )"},{"path":"/reference/preparePathways.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare miRNA-augmented pathways for integrative miRNA-mRNA pathway analyses — preparePathways","text":"mirnaObj MirnaExperiment object containing miRNA gene data database name database use. must one : KEGG, Reactome, WikiPathways. Default KEGG organism name organism consideration. different databases different supported organisms. see list supported organisms given database, use supportedOrganisms() function. Default specie Homo sapiens minPc minimum percentage measured features pathway must considered analysis. Default 10. See details section additional information BPPARAM desired parallel computing behavior. parameter defaults BiocParallel::bpparam(), can edited. See BiocParallel::bpparam() information parallel computing R","code":""},{"path":"/reference/preparePathways.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare miRNA-augmented pathways for integrative miRNA-mRNA pathway analyses — preparePathways","text":"list object containing miRNA-augmented pathways graphNEL objects.","code":""},{"path":"/reference/preparePathways.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Prepare miRNA-augmented pathways for integrative miRNA-mRNA pathway analyses — preparePathways","text":"create augmented pathways, function uses graphite R package download biological networks mentioned databases. , pathway converted graph object, significant miRNA-mRNA interactions added network. , edge weights added according interaction type. point, biological pathways nodes measured excluded analysis. required , differential expression analysis, lowly expressed features removed. Therefore, pathways might result significantly affected even 1% nodes perturbed. default behavior exclude pathways less 10% representation (minPc = 10). Finally, function performs breadth-first search (BFS) algorithm topologically sort pathway nodes individual node occurs upstream nodes. Nodes within cycles considered leaf nodes. Information pathway coverage, .e. percentage nodes expression measurments, edge weights, topological sorting order, parameters used create networks stored graphData slot graphNEL object.","code":""},{"path":"/reference/preparePathways.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Prepare miRNA-augmented pathways for integrative miRNA-mRNA pathway analyses — preparePathways","text":"Sales, G., Calura, E., Cavalieri, D. et al. graphite - Bioconductor package convert pathway topology gene network. BMC Bioinformatics 13, 20 (2012), https://doi.org/10.1186/1471-2105-13-20.","code":""},{"path":"/reference/preparePathways.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Prepare miRNA-augmented pathways for integrative miRNA-mRNA pathway analyses — preparePathways","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/preparePathways.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prepare miRNA-augmented pathways for integrative miRNA-mRNA pathway analyses — preparePathways","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # perform integration analysis with default settings obj <- mirnaIntegration(obj) #> Since data derive from paired samples, a correlation test will be used. #> Performing Spearman's correlation analysis... #> A statistically significant correlation between 215 miRNA-target pairs was found!  # \\donttest{ # retrieve pathways from KEGG and augment them with miRNA-gene interactions paths <- preparePathways(obj) #> Downloading pathways from KEGG database... #> Converting identifiers to gene symbols... #> Adding miRNA-gene interactions to biological pathways... #> Warning: 152 pathways have been ignored because they contain too few nodes with gene expression measurement. #> Performing topological sorting of pathway nodes...  # perform the integrative pathway analysis with 1000 permutations ipa <- topologicalAnalysis(obj, paths, nPerm = 1000) #> Calculating pathway scores... #> Generating random permutations... #> Calculating p-values with 1000 permutations... #> Correcting p-values through the max-T procedure... #> The topologically-aware integrative pathway analysis reported 2 significantly altered pathways!  # access the results of pathway analysis integratedPathways(ipa) #>                                             pathway  coverage    score #> Thyroid hormone synthesis Thyroid hormone synthesis 0.3469388 12.12941 #> Thyroid cancer                       Thyroid cancer 0.2820513 11.56291 #>                           normalized.score       P.Val adj.P.Val #> Thyroid hormone synthesis         8.418675 0.000999001     0.017 #> Thyroid cancer                    7.214414 0.000999001     0.042  # create a dotplot of integrated pathways integrationDotplot(ipa)   # explore a specific biological network visualizeNetwork(ipa, \"Thyroid hormone synthesis\")  # }"},{"path":"/reference/searchDisease.html","id":null,"dir":"Reference","previous_headings":"","what":"Search for disease EFO identifiers — searchDisease","title":"Search for disease EFO identifiers — searchDisease","text":"function allows retrieve Experimental Factor Ontology (EFO) identifier particular disease. ID needed use function findMirnaSNPs().","code":""},{"path":"/reference/searchDisease.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Search for disease EFO identifiers — searchDisease","text":"","code":"searchDisease(diseaseName)"},{"path":"/reference/searchDisease.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Search for disease EFO identifiers — searchDisease","text":"diseaseName name particular disease (ex. Alzheimer disease).","code":""},{"path":"/reference/searchDisease.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Search for disease EFO identifiers — searchDisease","text":"character object containing EFO identifiers.","code":""},{"path":"/reference/searchDisease.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Search for disease EFO identifiers — searchDisease","text":"retrieve EFO IDs specific diseases, function makes use gwasrapidd package.","code":""},{"path":"/reference/searchDisease.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Search for disease EFO identifiers — searchDisease","text":"Ramiro Magno, Ana-Teresa Maia, gwasrapidd: R package query, download wrangle GWAS catalog data, Bioinformatics, Volume 36, Issue 2, January 2020, Pages 649–650, https://doi.org/10.1093/bioinformatics/btz605.","code":""},{"path":"/reference/searchDisease.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Search for disease EFO identifiers — searchDisease","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/searchDisease.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Search for disease EFO identifiers — searchDisease","text":"","code":"# \\donttest{ # search the EFO identifier of Alzheimer disease searchDisease(\"Alzheimer disease\") #> Checking for cached EFO traits... #> Reading EFO traits from cache... #> Searching for disease: Alzheimer disease #> [1] \"Alzheimer's disease biomarker measurement\"  #> [2] \"Alzheimer's disease neuropathologic change\" #> [3] \"Alzheimer disease\"                          #> [4] \"late-onset Alzheimers disease\"              #> [5] \"family history of Alzheimer’s disease\"      #> [6] \"age of onset of Alzheimer disease\"          # }"},{"path":"/reference/significantAccessors.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the IDs of statistically differentially expressed miRNAs/genes — significantAccessors","title":"Get the IDs of statistically differentially expressed miRNAs/genes — significantAccessors","text":"significantMirnas() significantGenes() functions access significant features contained mirnaDE geneDE slots MirnaExperiment object, can used obtain IDs statistically differentially expressed miRNAs genes.","code":""},{"path":"/reference/significantAccessors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the IDs of statistically differentially expressed miRNAs/genes — significantAccessors","text":"","code":"significantMirnas(object)  significantGenes(object)"},{"path":"/reference/significantAccessors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the IDs of statistically differentially expressed miRNAs/genes — significantAccessors","text":"object MirnaExperiment object containing miRNA gene data","code":""},{"path":"/reference/significantAccessors.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the IDs of statistically differentially expressed miRNAs/genes — significantAccessors","text":"character vector miRNA IDs (e.g. 'hsa-miR-16-5p', hsa-miR-29a-3p'...), acharacter vector gene symbols (e.g. 'TP53', 'FOXP2', 'TIGAR', CASP1'...).","code":""},{"path":"/reference/significantAccessors.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Get the IDs of statistically differentially expressed miRNAs/genes — significantAccessors","text":"significantMirnas(): Get IDs differentially expressed miRNAs significantGenes(): Get IDs differentially expressed genes","code":""},{"path":"/reference/significantAccessors.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get the IDs of statistically differentially expressed miRNAs/genes — significantAccessors","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/significantAccessors.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get the IDs of statistically differentially expressed miRNAs/genes — significantAccessors","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # extract significant DE-miRNAs sigMirnas <- significantMirnas(obj)  # extract significant DEGs sigGenes <- significantGenes(obj)"},{"path":"/reference/supportedOrganisms.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the list of supported organisms for a given database — supportedOrganisms","title":"Get the list of supported organisms for a given database — supportedOrganisms","text":"function provides list supported organisms different databases, namely Gene Ontology (GO), Kyoto Encyclopedia Genes Genomes (KEGG), MsigDB, WikiPathways, Reactome, Enrichr, Disease Ontology (), Network Cancer Genes (NCG), DisGeNET, COVID19.","code":""},{"path":"/reference/supportedOrganisms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the list of supported organisms for a given database — supportedOrganisms","text":"","code":"supportedOrganisms(database)"},{"path":"/reference/supportedOrganisms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the list of supported organisms for a given database — supportedOrganisms","text":"database database name. must one : GO, KEGG, MsigDB, WikiPathways, Reactome, Enrichr, , NCG, DisGeNET, COVID19","code":""},{"path":"/reference/supportedOrganisms.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the list of supported organisms for a given database — supportedOrganisms","text":"character vector listing supported organisms database specified user.","code":""},{"path":"/reference/supportedOrganisms.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Get the list of supported organisms for a given database — supportedOrganisms","text":"perform functional enrichment genes, MIRit uses geneset R package download gene sets mentioned databases.","code":""},{"path":"/reference/supportedOrganisms.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Get the list of supported organisms for a given database — supportedOrganisms","text":"Liu, Y., Li, G. Empowering biologists decode omics data: Genekitr R package web server. BMC Bioinformatics 24, 214 (2023). https://doi.org/10.1186/s12859-023-05342-9.","code":""},{"path":"/reference/supportedOrganisms.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get the list of supported organisms for a given database — supportedOrganisms","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/supportedOrganisms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get the list of supported organisms for a given database — supportedOrganisms","text":"","code":"# get the supported organisms for GO database supportedOrganisms(\"GO\") #>   [1] \"Amborella trichopoda\"               \"Anolis carolinensis\"                #>   [3] \"Anopheles gambiae\"                  \"Aquifex aeolicus\"                   #>   [5] \"Arabidopsis thaliana\"               \"Ashbya gossypii\"                    #>   [7] \"Bacillus cereus\"                    \"Bacillus subtilis\"                  #>   [9] \"Bacteroides thetaiotaomicron\"       \"Batrachochytrium dendrobatidis\"     #>  [11] \"Bos taurus\"                         \"Brachypodium distachyon\"            #>  [13] \"Bradyrhizobium diazoefficiens\"      \"Branchiostoma floridae\"             #>  [15] \"Brassica napus\"                     \"Brassica rapa subsp. pekinensis\"    #>  [17] \"Caenorhabditis briggsae\"            \"Caenorhabditis elegans\"             #>  [19] \"Candida albicans\"                   \"Canis lupus familiaris\"             #>  [21] \"Capsicum annuum\"                    \"Chlamydia trachomatis\"              #>  [23] \"Chlamydomonas reinhardtii\"          \"Chloroflexus aurantiacus\"           #>  [25] \"Ciona intestinalis\"                 \"Citrus sinensis\"                    #>  [27] \"Clostridium botulinum\"              \"Coxiella burnetii\"                  #>  [29] \"Cryptococcus neoformans\"            \"Cucumis sativus\"                    #>  [31] \"Danio rerio\"                        \"Daphnia pulex\"                      #>  [33] \"Deinococcus radiodurans\"            \"Dictyoglomus turgidum\"              #>  [35] \"Dictyostelium discoideum\"           \"Dictyostelium purpureum\"            #>  [37] \"Drosophila melanogaster\"            \"Emericella nidulans\"                #>  [39] \"Entamoeba histolytica\"              \"Equus caballus\"                     #>  [41] \"Erythranthe guttata\"                \"Escherichia coli\"                   #>  [43] \"Eucalyptus grandis\"                 \"Felis catus\"                        #>  [45] \"Fusobacterium nucleatum\"            \"Gallus gallus\"                      #>  [47] \"Geobacter sulfurreducens\"           \"Giardia intestinalis\"               #>  [49] \"Gloeobacter violaceus\"              \"Glycine max\"                        #>  [51] \"Gorilla gorilla gorilla\"            \"Gossypium hirsutum\"                 #>  [53] \"Haemophilus influenzae\"             \"Halobacterium salinarum\"            #>  [55] \"Helianthus annuus\"                  \"Helicobacter pylori\"                #>  [57] \"helobdella robusta\"                 \"Homo sapiens\"                       #>  [59] \"Hordeum vulgare subsp. vulgare\"     \"Ixodes scapularis\"                  #>  [61] \"Juglans regia\"                      \"Klebsormidium nitens\"               #>  [63] \"Korarchaeum cryptofilum\"            \"Lactuca sativa\"                     #>  [65] \"Leishmania major\"                   \"lepisosteus oculatus\"               #>  [67] \"Leptospira interrogans\"             \"Listeria monocytogenes\"             #>  [69] \"Macaca mulatta\"                     \"Manihot esculenta\"                  #>  [71] \"Marchantia polymorpha\"              \"Medicago truncatula\"                #>  [73] \"Methanocaldococcus jannaschii\"      \"Methanosarcina acetivorans\"         #>  [75] \"Monodelphis domestica\"              \"Monosiga brevicollis\"               #>  [77] \"Mus musculus\"                       \"Musa acuminata subsp. malaccensis\"  #>  [79] \"Mycobacterium tuberculosis\"         \"mycoplasma genitalium\"              #>  [81] \"Neisseria meningitidis serogroup b\" \"Nelumbo nucifera\"                   #>  [83] \"Nematostella vectensis\"             \"Neosartorya fumigata\"               #>  [85] \"Neurospora crassa\"                  \"Nicotiana tabacum\"                  #>  [87] \"Nitrosopumilus maritimus\"           \"Ornithorhynchus anatinus\"           #>  [89] \"Oryza sativa\"                       \"Oryzias latipes\"                    #>  [91] \"Pan troglodytes\"                    \"Paramecium tetraurelia\"             #>  [93] \"Phaeosphaeria nodorum\"              \"Physcomitrella patens\"              #>  [95] \"Phytophthora ramorum\"               \"Plasmodium falciparum\"              #>  [97] \"Populus trichocarpa\"                \"Pristionchus pacificus\"             #>  [99] \"Prunus persica\"                     \"Pseudomonas aeruginosa\"             #> [101] \"Puccinia graminis\"                  \"Pyrobaculum aerophilum\"             #> [103] \"Rattus norvegicus\"                  \"Rhodopirellula baltica\"             #> [105] \"Ricinus communis\"                   \"Saccharomyces cerevisiae\"           #> [107] \"Salmonella typhimurium\"             \"Schizosaccharomyces japonicus\"      #> [109] \"Schizosaccharomyces pombe\"          \"Sclerotinia sclerotiorum\"           #> [111] \"Selaginella moellendorffii\"         \"Setaria italica\"                    #> [113] \"Shewanella oneidensis\"              \"Solanum lycopersicum\"               #> [115] \"Solanum tuberosum\"                  \"Sorghum bicolor\"                    #> [117] \"Spinacia oleracea\"                  \"Staphylococcus aureus\"              #> [119] \"Streptococcus pneumoniae\"           \"Streptomyces coelicolor\"            #> [121] \"Strongylocentrotus purpuratus\"      \"Sulfolobus solfataricus\"            #> [123] \"Sus scrofa\"                         \"Synechocystis\"                      #> [125] \"Thalassiosira pseudonana\"           \"Theobroma cacao\"                    #> [127] \"Thermococcus kodakaraensis\"         \"Thermodesulfovibrio yellowstonii\"   #> [129] \"Thermotoga maritima\"                \"Tribolium castaneum\"                #> [131] \"Trichomonas vaginalis\"              \"Trichoplax adhaerens\"               #> [133] \"Triticum aestivum\"                  \"Trypanosoma brucei\"                 #> [135] \"Ustilago maydis\"                    \"Vibrio cholerae\"                    #> [137] \"Vitis vinifera\"                     \"Xanthomonas campestris\"             #> [139] \"Xenopus tropicalis\"                 \"Yarrowia lipolytica\"                #> [141] \"Yersinia pestis\"                    \"Zea mays\"                           #> [143] \"Zostera marina\"                      # get the supported organisms for Reactome supportedOrganisms(\"Reactome\") #>  [1] \"Bos taurus\"               \"Caenorhabditis elegans\"   #>  [3] \"Danio rerio\"              \"Drosophila melanogaster\"  #>  [5] \"Gallus gallus\"            \"Homo sapiens\"             #>  [7] \"Mus musculus\"             \"Rattus norvegicus\"        #>  [9] \"Saccharomyces cerevisiae\" \"Sus scrofa\"               #> [11] \"Xenopus tropicalis\""},{"path":"/reference/topologicalAnalysis.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform a topologically-aware integrative pathway analysis (TAIPA) — topologicalAnalysis","title":"Perform a topologically-aware integrative pathway analysis (TAIPA) — topologicalAnalysis","text":"function allows perform integrative pathway analysis aims identify biological networks affected miRNomic transcriptomic dysregulations. function takes miRNA-augmented pathways, created preparePathways() function, calculates score estimates degree impairment pathway. Later, statistical significance calculated permutation test. main advantages method require matched samples, allows perform integrative miRNA-mRNA pathway analysis takes account topology biological networks. See details section additional information.","code":""},{"path":"/reference/topologicalAnalysis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform a topologically-aware integrative pathway analysis (TAIPA) — topologicalAnalysis","text":"","code":"topologicalAnalysis(   mirnaObj,   pathways,   pCutoff = 0.05,   pAdjustment = \"max-T\",   nPerm = 10000,   progress = FALSE,   tasks = 0,   BPPARAM = bpparam() )"},{"path":"/reference/topologicalAnalysis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform a topologically-aware integrative pathway analysis (TAIPA) — topologicalAnalysis","text":"mirnaObj MirnaExperiment object containing miRNA gene data pathways list miRNA-augmented pathways returned preparePathways() function pCutoff adjusted p-value cutoff use statistical significance. default value 0.05 pAdjustment p-value correction method multiple testing. must one : max-T (default), fdr, BH, none, holm, hochberg, hommel, bonferroni, nPerm number permutation used assessing statistical significance pathway. Default 10000. See details section additional information progress Logical, whether show progress bar p-value calculation . Default FALSE, include progress bar. Please note setting progress = TRUE high values tasks leads less efficient parallelization. See details section additional information tasks integer 0 100 specifies frequently progress bar must updated. Default 0 simply split computation among workers. High values tasks can lead 15-30% slower p-value calculation. See details section additional information BPPARAM desired parallel computing behavior. parameter defaults BiocParallel::bpparam(), can edited. See BiocParallel::bpparam() information parallel computing R","code":""},{"path":"/reference/topologicalAnalysis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform a topologically-aware integrative pathway analysis (TAIPA) — topologicalAnalysis","text":"object class IntegrativePathwayAnalysis stores results analysis. See relative help page details.","code":""},{"path":[]},{"path":"/reference/topologicalAnalysis.html","id":"topologically-aware-integrative-pathway-analysis-taipa-","dir":"Reference","previous_headings":"","what":"Topologically-Aware Integrative Pathway Analysis (TAIPA)","title":"Perform a topologically-aware integrative pathway analysis (TAIPA) — topologicalAnalysis","text":"analysis aims identify biological pathways result affected miRNA mRNA dysregulations. analysis, biological pathways retrieved pathway database KEGG, interplay miRNAs genes added networks. network defined graph \\(G(V, E)\\), \\(V\\) represents nodes, \\(E\\) represents relationships nodes. , nodes significantly differentially expressed assigned weight \\(w_i = 1\\), whereas differentially expressed nodes assigned weight \\(w_i = \\left| \\Delta E_i \\right|\\), \\(\\Delta E_i\\) linear fold change node. Moreover, consider biological interaction two nodes, namely \\(\\) \\(j\\), define interaction parameter \\(\\beta_{\\rightarrow j} = 1\\) activation interactions \\(\\beta_{\\rightarrow j} = -1\\) repression interactions. Subsequently, concordance coefficient \\(\\gamma_{\\rightarrow j}\\) defined : $$\\gamma_{\\rightarrow j} = \\begin{cases} \\beta_{\\rightarrow j} &\\text{} sign(\\Delta E_i) = sign(\\Delta E_j) \\\\ - \\beta_{\\rightarrow j} &\\text{} sign(\\Delta E_i) \\= sign(\\Delta E_j) \\end{cases}\\,.$$ Later process, breadth-first search (BFS) algorithm applied topologically sort pathway nodes individual node occurs upstream nodes. Nodes within cycles considered leaf nodes. point, node score \\(\\phi\\) calculated pathway node \\(\\) : $$\\phi_i = w_i + \\sum_{j=1}^{U} \\gamma_{\\rightarrow j} \\cdot k_j\\,.$$ \\(U\\) represents number upstream nodes, \\(\\gamma_{\\rightarrow j}\\) denotes concordance coefficient, \\(k_j\\) propagation factor defined : $$k_j = \\begin{cases} w_j &\\text{} \\phi_j = 0 \\\\ \\phi_j &\\text{} \\phi_j \\= 0 \\end{cases}\\,.$$ Finally, pathway score \\(\\Psi\\) calculated : $$\\Psi = \\frac{1 - M}{N} \\cdot \\sum_{=1}^{N} \\phi_i\\,,$$ \\(M\\) represents proportion miRNAs pathway, \\(N\\) represents total number nodes network. , compute statistical significance pathway score, permutation procedure applied. Later, observed pathway scores permuted scores standardized subtracting mean score permuted sets \\(\\mu_{\\Psi_P}\\) dividing standard deviation permuted scores \\(\\sigma_{\\Psi_P}\\). Finally, p-value defined based fraction permutations reported higher normalized pathway score observed one. However, prevent p-values equal zero, define p-values : $$p = \\frac{\\sum_{n=1}^{N_p} \\left[ \\Psi_{P_N} \\ge \\Psi_N \\right] + 1} {N_p + 1}\\,.$$ end, p-values corrected multiple testing either max-T procedure (default option) particularly suited permutation tests, standard multiple testing approaches.","code":""},{"path":"/reference/topologicalAnalysis.html","id":"implementation-details","dir":"Reference","previous_headings":"","what":"Implementation details","title":"Perform a topologically-aware integrative pathway analysis (TAIPA) — topologicalAnalysis","text":"computational efficiency, pathway score computation implemented C++ language. Moreover, define statistical significance network, permutation test applied following number permutations specified nPerm. default setting perform 10000 permutations. higher number permutations, stable calculated p-values, even though time needed increase. regard, since computing pathway score 10000 networks pathway computationally intensive, parallel computing employed reduce running time. user can modify parallel computing behavior specifying BPPARAM parameter. See BiocParallel::bpparam() details. , progress bar can also included show completion percentage setting progress = TRUE. Moreover, user can define frequently progress bar gets updated tweaking tasks parameter. using progress = TRUE, setting tasks 100 tells function update progress bar 100 times, user can see increases 1%. Instead, setting tasks 50, means progress bar gets updated every 2% completion. However, keep mind tasks values 50 100 lead 15-30% slower p-value calculation due increased data transfer workers. Instead, lower tasks values like 20 determine less frequent progress updates slightly less efficient including progress bar.","code":""},{"path":"/reference/topologicalAnalysis.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Perform a topologically-aware integrative pathway analysis (TAIPA) — topologicalAnalysis","text":"Peter H. Westfall S. Stanley Young. Resampling-Based Multiple Testing: Examples Methods p-Value Adjustment. John Wiley & Sons. ISBN 978-0-471-55761-6.","code":""},{"path":"/reference/topologicalAnalysis.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Perform a topologically-aware integrative pathway analysis (TAIPA) — topologicalAnalysis","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/topologicalAnalysis.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform a topologically-aware integrative pathway analysis (TAIPA) — topologicalAnalysis","text":"","code":"# load example MirnaExperiment object obj <- loadExamples()  # perform integration analysis with default settings obj <- mirnaIntegration(obj) #> Since data derive from paired samples, a correlation test will be used. #> Performing Spearman's correlation analysis... #> A statistically significant correlation between 215 miRNA-target pairs was found!  # \\donttest{ # retrieve pathways from KEGG and augment them with miRNA-gene interactions paths <- preparePathways(obj) #> Downloading pathways from KEGG database... #> Converting identifiers to gene symbols... #> Adding miRNA-gene interactions to biological pathways... #> Warning: 152 pathways have been ignored because they contain too few nodes with gene expression measurement. #> Performing topological sorting of pathway nodes...  # perform the integrative pathway analysis with 1000 permutations ipa <- topologicalAnalysis(obj, paths, nPerm = 1000) #> Calculating pathway scores... #> Generating random permutations... #> Calculating p-values with 1000 permutations... #> Correcting p-values through the max-T procedure... #> The topologically-aware integrative pathway analysis reported 2 significantly altered pathways!  # access the results of pathway analysis integratedPathways(ipa) #>                                             pathway  coverage    score #> Thyroid hormone synthesis Thyroid hormone synthesis 0.3469388 12.12941 #> Thyroid cancer                       Thyroid cancer 0.2820513 11.56291 #>                           normalized.score       P.Val adj.P.Val #> Thyroid hormone synthesis         8.409300 0.000999001     0.020 #> Thyroid cancer                    7.845217 0.001998002     0.033  # create a dotplot of integrated pathways integrationDotplot(ipa)   # explore a specific biological network visualizeNetwork(ipa, \"Thyroid hormone synthesis\")  # }"},{"path":"/reference/visualizeNetwork.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize the relationships between miRNAs and genes in a biological pathway — visualizeNetwork","title":"Visualize the relationships between miRNAs and genes in a biological pathway — visualizeNetwork","text":"function can used plot augmented pathways created topologicalAnalysis() function. particular, given valid object class IntegrativePathwayAnalysis, function allows produce network graph specified biological pathway, alongside expression fold changes. way, augmented pathways made miRNAs genes can visually explored better investigate consequences miRNA/gene dysregulations.","code":""},{"path":"/reference/visualizeNetwork.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize the relationships between miRNAs and genes in a biological pathway — visualizeNetwork","text":"","code":"visualizeNetwork(   object,   pathway,   algorithm = \"dot\",   fontsize = 14,   lfcScale = c(\"royalblue\", \"white\", \"red\"),   nodeBorderCol = \"black\",   nodeTextCol = \"black\",   edgeCol = \"darkgrey\",   edgeWidth = 1,   subgraph = NULL,   highlightNodes = NULL,   highlightCol = \"gold\",   highlightWidth = 2,   legendColorbar = TRUE,   legendInteraction = TRUE,   title = NULL,   titleCex = 2,   titleFace = 1 )"},{"path":"/reference/visualizeNetwork.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize the relationships between miRNAs and genes in a biological pathway — visualizeNetwork","text":"object object class IntegrativePathwayAnalysis containing results miRNA-mRNA pathway analysis pathway name biological pathway show. available pathways given database can seen listPathways() function algorithm layout algorithm used arrange nodes network. must one dot (default), circo, fdp, neato, osage twopi. information regarding algorithms, please check details section fontsize font size node graph. Default 14 lfcScale must character vector length 3 containing valid R color names creating gradient log2 fold changes. first value refers downregulation, middle one stable expression, last one upregulation. Default value c('royalblue', 'white', 'red'). Available color formats include color names, 'blue' 'red', hexadecimal colors specified #RRGGBB nodeBorderCol must R color name specifies color node borders. Default black. Available color formats include color names, 'blue' 'red', hexadecimal colors specified #RRGGBB nodeTextCol must R color name specifies color miRNA/gene names. Default black. Available color formats include color names, 'blue' 'red', hexadecimal colors specified #RRGGBB edgeCol must R color name specifies color edges nodes. Default darkgrey. Available color formats include color names, 'blue' 'red', hexadecimal colors specified #RRGGBB edgeWidth width edges. Default 1 subgraph optional character vector containing nodes want maintain final plot. nodes shown. useful display specific features extremely messy graphs. Default NULL highlightNodes character vector containing names nodes want highlight. Default NULL highlight nodes. See details section additional information highlightCol must R color name specifies color edges borders highlighted nodes. Default gold. Available color formats include color names, 'blue' 'red', hexadecimal colors specified #RRGGBB highlightWidth width edges highlighted nodes. Default 2 legendColorbar Logical, whether add legend color bar log2 fold changes. Default TRUE legendInteraction Logical, whether add legend links edge types biological interactions. Default TRUE title title plot. Default NULL include plot title titleCex cex plot main title. Default 2 titleFace integer specifies font use title. 1 corresponds plain text, 2 bold face, 3 italic, 4 bold italic, 5 symbol font. Default 1","code":""},{"path":"/reference/visualizeNetwork.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Visualize the relationships between miRNAs and genes in a biological pathway — visualizeNetwork","text":"base R plot augmented pathway.","code":""},{"path":"/reference/visualizeNetwork.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Visualize the relationships between miRNAs and genes in a biological pathway — visualizeNetwork","text":"network created function highly flexible, allowing tweak different parameters can influence resulting graph, including node highlighting, layout algorithms, colors, legends.","code":""},{"path":"/reference/visualizeNetwork.html","id":"nodes-included-in-the-plot","dir":"Reference","previous_headings":"","what":"Nodes included in the plot","title":"Visualize the relationships between miRNAs and genes in a biological pathway — visualizeNetwork","text":"huge messy networks, user can specify nodes include plot subgraph parameter, order represent features wants display. Alternatively, parameter can set NULL (default), plot nodes biological pathway.","code":""},{"path":"/reference/visualizeNetwork.html","id":"highlight-nodes-and-edges","dir":"Reference","previous_headings":"","what":"Highlight nodes and edges","title":"Visualize the relationships between miRNAs and genes in a biological pathway — visualizeNetwork","text":"One interesting feature offered function consists highlighting specific nodes edges within network. results particularly useful want put evidence affected routes biological pathway. highlight nodes, must provide highlightNodes parameter character vector lists desired nodes. result, borders highlighted nodes colored according highlightCol parameter (default 'gold'), width specified highlightWidth (default 2). Notably, function automatically highlights way edges connecting selected nodes.","code":""},{"path":"/reference/visualizeNetwork.html","id":"layout-algorithms","dir":"Reference","previous_headings":"","what":"Layout algorithms","title":"Visualize the relationships between miRNAs and genes in a biological pathway — visualizeNetwork","text":"Furthermore, function allows use different methods lay nodes network setting algorithm parameter. regard, several algorithms Rgraphviz package can used, namely: dot (default), algorithm attributed Sugiyama et al. described Gansner et al., creates ranked layout particularly suited display hierarchies complex pathways; circo, uses recursive radial algorithm resulting circular layout; fdp, adopts force-directed approach similar Fruchterman Reingold; neato, relies spring model iterative solver finds low energy configurations; osage, layout engine recursively draws cluster subgraphs; twopi, places node center network, arranges remaining nodes series concentric circles around center. additional information algorithms, refer Rgraphviz::GraphvizLayouts.","code":""},{"path":"/reference/visualizeNetwork.html","id":"customization","dir":"Reference","previous_headings":"","what":"Customization","title":"Visualize the relationships between miRNAs and genes in a biological pathway — visualizeNetwork","text":"customize look resulting plot, function allows change different graphical parameters, including: color scale log2 fold changes, can set lfcScale; font size nodes, can changed fontsize; border color nodes, can edited nodeBorderCol; text color nodes, can changed nodeTextCol; color used edges, set edgeCol; width edges, customizable edgeWidth. Additionally, function allows include handy legends useful interpreting biological consequences network alterations. particular: color bar legend displaying log2 fold changes corresponding fill color can included legendColorbar = TRUE (default); legend links appearance edges arrow heads type biological interaction can shown legendInteraction = TRUE (default). Lastly, title, titleCex titleFace parameters can tweaked include network title desired look.","code":""},{"path":"/reference/visualizeNetwork.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Visualize the relationships between miRNAs and genes in a biological pathway — visualizeNetwork","text":"function uses Rgraphviz package render network object.","code":""},{"path":"/reference/visualizeNetwork.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Visualize the relationships between miRNAs and genes in a biological pathway — visualizeNetwork","text":"Jacopo Ronchi, jacopo.ronchi@unimib.","code":""},{"path":"/reference/visualizeNetwork.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualize the relationships between miRNAs and genes in a biological pathway — visualizeNetwork","text":"","code":"# load example IntegrativePathwayAnalysis object obj <- loadExamples(\"IntegrativePathwayAnalysis\")  # \\donttest{ # explore a specific biological network visualizeNetwork(obj, \"Thyroid hormone synthesis\")  # }"},{"path":"/news/index.html","id":"mirit-0990","dir":"Changelog","previous_headings":"","what":"MIRit 0.99.0","title":"MIRit 0.99.0","text":"Initial version Bioconductor submission.","code":""}]