A-app-faq.Rmd

# (APPENDIX) Appendix {-}

# Frequently asked questions {#faq}

## How to prepare your own geneList {#genelist}

GSEA analysis requires a ranked gene list, which contains three features:


+ numeric vector: fold change or other type of numerical variable
+ named vector: every number has a name, the corresponding gene ID
+ sorted vector: number should be sorted in decreasing order


If you import your data from a `csv` file, the file should contains two columns, one for gene ID (no duplicated ID allowed) and another one for fold change. You can prepare your own `geneList` via the following command:


```r
d = read.csv(your_csv_file)
## assume 1st column is ID
## 2nd column is FC

## feature 1: numeric vector
geneList = d[,2]

## feature 2: named vector
names(geneList) = as.character(d[,1])

## feature 3: decreasing orde
geneList = sort(geneList, decreasing = TRUE)
```



<!--


# DAVID functional analysis

[clusterProfiler](https://www.bioconductor.org/packages/clusterProfiler) provides enrichment and GSEA analysis with GO, KEGG, DO and Reactome pathway supported internally, some user may prefer GO and KEGG analysis with DAVID[@huang_david_2007] and still attracted by the visualization methods provided by [clusterProfiler](https://www.bioconductor.org/packages/clusterProfiler)[@paranjpe_genome_wid_2013]. To bridge the gap between DAVID and clusterProfiler, we implemented `enrichDAVID`. This function query enrichment analysis result from DAVID webserver via [RDAVIDWebService](https://www.bioconductor.org/packages/RDAVIDWebService)[@fresno_rdavidwebservice_2013] and stored the result as an `enrichResult` instance, so that we can use all the visualization functions in [clusterProfiler](https://www.bioconductor.org/packages/clusterProfiler) to visualize DAVID results. `enrichDAVID` is fully compatible with `compareCluster` function and comparing enrichment results from different gene clusters is now available with DAVID.

```{r eval=FALSE}
library(clusterProfiler)
data(geneList, package="DOSE")
gene = names(geneList)[abs(geneList) > 2]
david <- enrichDAVID(gene = gene,
                     idType = "ENTREZ_GENE_ID",
                     listType = "Gene",
                     annotation = "KEGG_PATHWAY",
                     david.user = "clusterProfiler@hku.hk")
```

DAVID Web Service has the following limitations:

+ A job with more than 3000 genes to generate gene or term cluster report will not be handled by DAVID due to resource limit.
+ No more than 200 jobs in a day from one user or computer.
+ DAVID Team reserves right to suspend any improper uses of the web service without notice.

For more details, please refer to [http://david.abcc.ncifcrf.gov/content.jsp?file=WS.html](http://david.abcc.ncifcrf.gov/content.jsp?file=WS.html).

As user has limited usage, please [register](http://david.abcc.ncifcrf.gov/webservice/register.htm) and use your own user account to run `enrichDAVID`.


-->


## No gene can be mapped

+ <https://www.biostars.org/p/431270/>
+ <https://github.com/YuLab-SMU/clusterProfiler/issues/280>


## Showing specific pathways {#showing-specific-pathways}

By default, all the visualization methods provided by `r Biocpkg("enrichplot")` display most significant pathways. If users are interested to show some specific pathways (e.g. excluding some unimportant pathways among the top categories), users can pass a vector of selected pathways to the `showCategory` parameter in `dotplot()`, `barplot()`, `treeplot()`, `cnetplot()` and `emapplot()` etc.


(ref:selectedPathsscap) Showing specific pathways.

(ref:selectedPathscap) **Showing specific pathways.** Top ten most significant pathways (A), selected ten pathways (B).

```{r selectedPaths, fig.height=6, fig.width=15, fig.cap="(ref:selectedPathscap)", fig.scap="(ref:selectedPathsscap)"}
library(DOSE)
library(enrichplot)
data(geneList)
de <- names(geneList)[1:100]

x <- enrichDO(de)

## show top 10 most significant pathways and want to exclude the second one
## dotplot(x, showCategory = x$Description[1:10][-2])

set.seed(2020-10-27)
selected_pathways <- sample(x$Description, 10)
selected_pathways

p1 <- dotplot(x, showCategory = 10, font.size=14)
p2 <- dotplot(x, showCategory = selected_pathways, font.size=14)


cowplot::plot_grid(p1, p2, labels=LETTERS[1:2])
```

Note: Another solution is using the `filter` verb to extract a subset of the result as described in [Chapter 16](#clusterProfiler-dplyr).


## How to extract genes of a specific term/pathway 

```{r}
id <- x$ID[1:3]
id
x[[id[1]]]
geneInCategory(x)[id]
```


## Wrap long axis labels {#label-format}

Most of the functions in `r Biocpkg("enrichplot")` can automatically split long labels across multiple lines. Users can passed a line width to the `label_format` parameter (default is 30). It also supports user defined function to format label strings. 

(ref:formatLabelscap) Wrap long axis labels.

(ref:formatLabelcap) **Wrap long axis labels.** Passing a numeric value to specify string width (A), a user specifiable labeller function (B).

```{r formatLabel, fig.height=6, fig.width=10, fig.cap="(ref:formatLabelcap)", fig.scap="(ref:formatLabelscap)"}
library(ReactomePA)
y <- enrichPathway(de)

p1 <- dotplot(y, label_format = 20) 
p2 <- dotplot(y, label_format = function(x) stringr::str_wrap(x, width=20))
cowplot::plot_grid(p1, p2, ncol=2, labels=c("A", "B")) 
```


The `label_format` option works with `barplot()`, `dotplot()`, `heatplot()`, `treeplot` and `ridgeplot()`.