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<meta http-equiv="Content-Type" content="text/html;charset=utf-8" />
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<title>too-many-cells</title>
<meta name="author" content="Gregory W. Schwartz" />
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<div id="content" class="content">
<h1 class="title">too-many-cells</h1>
<div id="table-of-contents" role="doc-toc">
<h2>Table of Contents</h2>
<div id="text-table-of-contents" role="doc-toc">
<ul>
<li><a href="#orgcce20f5">1. Description</a></li>
<li><a href="#org6992a13">2. New features for v3.0.0.0</a></li>
<li><a href="#org43dc2d3">3. New features for v2.2.0.0</a></li>
<li><a href="#org40cd514">4. New features for v2.0.0.0</a></li>
<li><a href="#orgec216f4">5. New features since initial launch</a></li>
<li><a href="#org4d8bb01">6. Installation</a>
<ul>
<li><a href="#org2a4d583">6.1. nix</a></li>
<li><a href="#org76a491e">6.2. Stack (unsupported in <code>too-many-cells >= v2.0.0.0</code>, use nix)</a>
<ul>
<li><a href="#org95b6d2d">6.2.1. Dependencies</a></li>
<li><a href="#orge1f3094">6.2.2. Install <code>stack</code></a></li>
<li><a href="#org7560e2e">6.2.3. Install <code>too-many-cells</code></a></li>
</ul>
</li>
<li><a href="#org29dd1b4">6.3. Docker</a></li>
</ul>
</li>
<li><a href="#org38b35bd">7. Troubleshooting</a>
<ul>
<li><a href="#org31b88a0">7.1. Using nix, I'm getting shared object not found errors.</a></li>
<li><a href="#orgd655944">7.2. I am getting errors like <code>AesonException "Error in $.packages.cassava.constraints.flags...</code> when running <code>stack</code> commands</a></li>
<li><a href="#org74de1e9">7.3. I use conda or custom ld library locations and I cannot install <code>too-many-cells</code> or run into weird R errors</a></li>
<li><a href="#org2478aec">7.4. I am still having issues with installation</a></li>
<li><a href="#orgdf2bfb6">7.5. I am on macOS/Windows with docker and <code>too-many-cells</code> silently crashes.</a></li>
<li><a href="#org2801396">7.6. I am getting the error <code>--draw-leaf</code> cannot be read, but I copied the command!</a></li>
</ul>
</li>
<li><a href="#org4549d0d">8. Included projects</a></li>
<li><a href="#org4f4fa59">9. Usage</a>
<ul>
<li><a href="#makeTreeUsage">9.1. <code>make-tree</code></a>
<ul>
<li><a href="#orgeb67830">9.1.1. Output</a></li>
<li><a href="#orgb2d1b3c">9.1.2. Outline with options</a></li>
<li><a href="#org05486bc">9.1.3. Example</a></li>
</ul>
</li>
<li><a href="#org20b1b06">9.2. <code>interactive</code></a></li>
<li><a href="#orge81a340">9.3. <code>differential</code></a></li>
<li><a href="#orgb2da5a5">9.4. <code>diversity</code></a></li>
<li><a href="#org1e4d537">9.5. <code>paths</code></a></li>
<li><a href="#too-many-peaks">9.6. Working with scATAC-seq data using <code>too-many-peaks</code></a></li>
<li><a href="#orgabc2989">9.7. <code>peaks</code></a></li>
<li><a href="#org9ad7ebb">9.8. <code>motifs</code></a></li>
<li><a href="#orga4ec4f5">9.9. <code>classify</code></a></li>
<li><a href="#spatial">9.10. <code>spatial</code></a></li>
<li><a href="#org34fb3c4">9.11. <code>matrix-output</code></a></li>
</ul>
</li>
<li><a href="#orgbc82a8e">10. Advanced documentation</a></li>
<li><a href="#orgb860e2b">11. Demo</a></li>
</ul>
</div>
</div>
<p>
<a href="https://gregoryschwartz.github.io/too-many-cells/">Website</a>
</p>
<p>
See <a href="https://github.com/GregorySchwartz/too-many-cells">https://github.com/GregorySchwartz/too-many-cells</a> for latest version. See <a href="#too-many-peaks">
<code>too-many-peaks</code> </a> for more information about scATAC-seq usage. See <a href="#spatial"> <code>spatial</code> </a> for
more information about spatial usage.
</p>
<p>
See <a href="https://doi.org/10.1038/s41592-020-0748-5">the publication</a> (and please cite!) for more information about the algorithm.
</p>
<div id="orgc3da573" class="figure">
<p><img src="img/pruned_tree.png" alt="pruned_tree.png" />
</p>
</div>
<div id="outline-container-orgcce20f5" class="outline-2">
<h2 id="orgcce20f5"><span class="section-number-2">1.</span> Description</h2>
<div class="outline-text-2" id="text-1">
<p>
<code>too-many-cells</code> is a suite of tools, algorithms, and visualizations focusing on
the relationships between cell clades. This includes new ways of clustering,
plotting, choosing differential expression comparisons, and more! While
<code>too-many-cells</code> was intended for single cell RNA-seq, any abundance data in any
domain can be used. Rather than opt for a unique positioning of each cell using
dimensionality reduction approaches like t-SNE, UMAP, and LSA, <code>too-many-cells</code>
recursively divides cells into clusters and relates clusters rather than
individual cells. In fact, by recursively dividing until further dividing would
be considered noise or random partitioning, we can eliminate noisy relationships
at the fine-grain level. The resulting binary tree serves as a basis for a
different perspective of single cells, using our <a href="http://github.com/GregorySchwartz/birch-beer#readme"> <code>birch-beer</code> </a> visualization
and tree measures to describe simultaneously large and small populations,
without additional parameters or runs. See below for a full list of features.
</p>
</div>
</div>
<div id="outline-container-org6992a13" class="outline-2">
<h2 id="org6992a13"><span class="section-number-2">2.</span> New features for v3.0.0.0</h2>
<div class="outline-text-2" id="text-2">
<ul class="org-ul">
<li>Added new <code>spatial</code> entry point for spatial analysis of cells! Can make
interactive plots of the cells in-situ with their features as well as quantify
spatial relationships between pairs of cells.</li>
<li>Overhauled the command line interface, so expect to find possible instability
with the options. Open an issue at
<a href="https://github.com/GregorySchwartz/too-many-cells/issues">https://github.com/GregorySchwartz/too-many-cells/issues</a> if you encounter any
expected errors or behavior!</li>
<li>Added MinMaxNorm for min-max normalization and TransposeNorm to transpose the
matrix to apply normalizations back and forth between axes, for instance,
<code>--normalization QuantileNorm --normalization TransposeNorm --normalization
MinMaxNorm --normalization TransposeNorm</code> will first apply quantile
normalization to each cell, then min-max normalization to each column (before
returning the cells to the proper axis with another tranpose).</li>
<li>Incompatibility: Projection file format changed "barcode" column to "item".</li>
</ul>
</div>
</div>
<div id="outline-container-org43dc2d3" class="outline-2">
<h2 id="org43dc2d3"><span class="section-number-2">3.</span> New features for v2.2.0.0</h2>
<div class="outline-text-2" id="text-3">
<ul class="org-ul">
<li><code>--no-edger</code> replaced with <code>--edger</code> as the default is now Kruskal-Wallis.</li>
<li>Can now use backgrounds for motifs.</li>
<li>Can specify motif for genome analysis (i.e. <code>findMotifsGenome.pl</code> from HOMER).</li>
<li>Temporary directories are now variables to correctly specify location.</li>
<li>Added q-values for differential.</li>
<li>Updated documentation for <code>too-many-peaks</code>.</li>
</ul>
</div>
</div>
<div id="outline-container-org40cd514" class="outline-2">
<h2 id="org40cd514"><span class="section-number-2">4.</span> New features for v2.0.0.0</h2>
<div class="outline-text-2" id="text-4">
<ul class="org-ul">
<li>Support for scATAC-seq for chromatin state relationships with <a href="#too-many-peaks"> <code>too-many-peaks</code> </a>!</li>
<li>Find enchriched regions as peaks for scATAC-seq with <code>peaks</code>.</li>
<li>Find motifs from differential chromatin state using <code>motifs</code>.</li>
<li>Linear relationships across the tree as pseudotime with <code>paths</code>.</li>
<li>Classify single-cell data from bulk with <code>classify</code>.</li>
<li>New dimensionality reductions with <code>--lsa</code>.</li>
<li>Output transformed matrix with <code>matrix-output</code>.</li>
<li>Bypass <code>labels.csv</code> with <code>-Z</code> quick labels.</li>
<li>MADs-from-median-based thresholds for multi-gene overlay plots</li>
<li>Multiple normalization application</li>
<li>And much more!</li>
</ul>
</div>
</div>
<div id="outline-container-orgec216f4" class="outline-2">
<h2 id="orgec216f4"><span class="section-number-2">5.</span> New features since initial launch</h2>
<div class="outline-text-2" id="text-5">
<ul class="org-ul">
<li>Now packaged for the functional package manager <code>nix</code> (Linux only)! No more dependency
shuffling or root for Docker needed!</li>
<li>A new R wrapper was written to quickly get data to and from <code>too-many-cells</code>
from R. <a href="https://github.com/GregorySchwartz/tooManyCellsR">Check it out here</a>!</li>
<li>Now works with Cellranger 3.0 matrices in addition to Cellranger 2.0</li>
<li>Can prune (make into leaves) specified nodes with <code>--custom-cut</code>.</li>
<li>Can analyze sets of features averaged together (e.g. gene sets). <b>Breaks API</b>,
so update your <code>--draw-leaf "DrawItem (DrawContinuous \"Cd4\")"</code> argument to
<code>--draw-leaf "DrawItem (DrawContinuous [\"Cd4\"])"</code> (notice the list
notation).</li>
<li>Outputs values from differential entry point plots (from <code>--features</code>), and can
aggregate features by average.</li>
</ul>
</div>
</div>
<div id="outline-container-org4d8bb01" class="outline-2">
<h2 id="org4d8bb01"><span class="section-number-2">6.</span> Installation</h2>
<div class="outline-text-2" id="text-6">
<p>
We provide multiple ways to install <code>too-many-cells</code>. We recommend installing
with <a href="#org2a4d583"> <code>nix</code> </a>. <code>nix</code> will provide all dependencies in the build, supports Linux,
and should be reproducible, so try that first. We also have <a href="#org29dd1b4">docker</a> images and a
<code>Dockerfile</code> to use in any system in case you have a custom build (for instance,
a non-standard R installation) or difficulty installing. <b>macOS and Windows
users:</b> <code>too-many-cells</code> was built and tested on Linux, so we highly recommend
using the <a href="#org29dd1b4">docker</a> image (which is a completely isolated environment which
requires no compiling or installation, other than docker itself) as there may be
difficulties in installing the dependencies.
</p>
</div>
<div id="outline-container-org2a4d583" class="outline-3">
<h3 id="org2a4d583"><span class="section-number-3">6.1.</span> nix</h3>
<div class="outline-text-3" id="text-6-1">
<p>
<code>too-many-cells</code> can be installed using the functional package manager <a href="https://nixos.org/nix/"> <code>nix</code> </a>.
While you will need <code>sudo</code> to install, no <code>sudo</code> is required after the correct
setup. First, install <code>nix</code> following the instructions
<a href="https://nixos.org/nix/">on the website</a>. Then, with an unset <code>LD_LIBRARY_PATH</code>,
</p>
<div class="org-src-container">
<pre class="src src-sh">git clone https://github.com/GregorySchwartz/too-many-cells.git
<span style="color: #ff8700;">cd</span> too-many-cells
nix-env -f default.nix -i too-many-cells
</pre>
</div>
</div>
</div>
<div id="outline-container-org76a491e" class="outline-3">
<h3 id="org76a491e"><span class="section-number-3">6.2.</span> Stack (unsupported in <code>too-many-cells >= v2.0.0.0</code>, use nix)</h3>
<div class="outline-text-3" id="text-6-2">
</div>
<div id="outline-container-org95b6d2d" class="outline-4">
<h4 id="org95b6d2d"><span class="section-number-4">6.2.1.</span> Dependencies</h4>
<div class="outline-text-4" id="text-6-2-1">
<p>
You may require the following dependencies to build and run (from Ubuntu 14.04,
use the appropriate packages from your distribution of choice):
</p>
<ul class="org-ul">
<li>build-essential</li>
<li>libgmp-dev</li>
<li>libblas-dev</li>
<li>liblapack-dev</li>
<li>libgsl-dev</li>
<li>libgtk2.0-dev</li>
<li>libcairo2-dev</li>
<li>libpango1.0-dev</li>
<li>graphviz</li>
<li>r-base</li>
<li>r-base-dev</li>
</ul>
<p>
To install them, in Ubuntu:
</p>
<div class="org-src-container">
<pre class="src src-shell">sudo apt install build-essential libgmp-dev libblas-dev liblapack-dev libgsl-dev libgtk2.0-dev libcairo2-dev libpango1.0-dev graphviz r-base r-base-dev
</pre>
</div>
<p>
<code>too-many-cells</code> also uses the following packages from R:
</p>
<ul class="org-ul">
<li>cowplot</li>
<li>ggplot2</li>
<li>edgeR</li>
<li>jsonlite</li>
</ul>
<p>
To install them in R,
</p>
<div class="org-src-container">
<pre class="src src-R">install.packages<span style="color: #008787;">(</span>c<span style="color: #d75f87;">(</span><span style="color: #afaf00;">"ggplot2"</span>, <span style="color: #afaf00;">"cowplot"</span>, <span style="color: #afaf00;">"jsonlite"</span><span style="color: #d75f87;">)</span><span style="color: #008787;">)</span>
install.packages<span style="color: #008787;">(</span><span style="color: #afaf00;">"BiocManager"</span><span style="color: #008787;">)</span>
BiocManager::install<span style="color: #008787;">(</span><span style="color: #afaf00;">"edgeR"</span><span style="color: #008787;">)</span>
</pre>
</div>
</div>
</div>
<div id="outline-container-orge1f3094" class="outline-4">
<h4 id="orge1f3094"><span class="section-number-4">6.2.2.</span> Install <code>stack</code></h4>
<div class="outline-text-4" id="text-6-2-2">
<p>
See <a href="https://docs.haskellstack.org/en/stable/README/">https://docs.haskellstack.org/en/stable/README/</a> for more details.
</p>
<div class="org-src-container">
<pre class="src src-sh">curl -sSL https://get.haskellstack.org/ | sh
stack setup
</pre>
</div>
</div>
</div>
<div id="outline-container-org7560e2e" class="outline-4">
<h4 id="org7560e2e"><span class="section-number-4">6.2.3.</span> Install <code>too-many-cells</code></h4>
<div class="outline-text-4" id="text-6-2-3">
</div>
<ol class="org-ol">
<li><a id="orgdc58dd1"></a>Source<br />
<div class="outline-text-5" id="text-6-2-3-1">
<p>
Probably the easiest method if you don't want to mess with dependencies (outside
of the ones above).
</p>
<div class="org-src-container">
<pre class="src src-sh">git clone https://github.com/GregorySchwartz/too-many-cells.git
<span style="color: #ff8700;">cd</span> too-many-cells
stack install
</pre>
</div>
</div>
</li>
<li><a id="org9435c45"></a>Online<br />
<div class="outline-text-5" id="text-6-2-3-2">
<p>
We only require <code>stack</code> (or <code>cabal</code>), you do not need to download any source
code (but you might need the stack.yaml.old dependency versions), just run the
following command to place <code>too-many-cells < v2.0.0.0</code> in your <code>~/.local/bin/</code>:
</p>
<div class="org-src-container">
<pre class="src src-sh">mv stack.yaml.preV2 stack.yaml
stack install too-many-cells
</pre>
</div>
<p>
If you run into errors like <code>Error: While constructing the build plan, the
following exceptions were encountered:</code>, then follow the advice. Usually you
just need to follow the suggestion and add the dependencies to the specified
file. For a quick <code>yaml</code> configuration, refer to
<a href="https://github.com/GregorySchwartz/too-many-cells/blob/master/stack.yaml.old">https://github.com/GregorySchwartz/too-many-cells/blob/master/stack.yaml.old</a>.
</p>
</div>
</li>
<li><a id="macOS"></a>macOS<br />
<div class="outline-text-5" id="text-macOS">
<p>
We recommend using <a href="#org29dd1b4">docker</a> on macOS. The following is written for
<code>too-many-cells < v2.0.0.0</code>. If you must compile
<code>too-many-cells</code>, you should get the above dependencies. For some dependencies,
you can use <a href="https://brew.sh/">brewer</a>, then install <code>too-many-cells</code> (in the cloned folder, don't
forget to install the R dependencies above):
</p>
<div class="org-src-container">
<pre class="src src-shell">brew cask install xquartz
brew install glib cairo gtk gettext fontconfig freetype
brew tap brewsci/bio
brew tap brewsci/science
brew install r zeromq graphviz pkg-config gsl libffi gobject-introspection gtk+ gtk+3
<span style="color: #767676;"># </span><span style="color: #767676;">Needed so pkg-config and libraries can be found.</span>
<span style="color: #767676;"># </span><span style="color: #767676;">For the second path, use the ouput of "brew info libffi".</span>
<span style="color: #ff8700;">export</span> <span style="color: #87afaf;">PKG_CONFIG_PATH</span>=/usr/local/lib/pkgconfig:/usr/local/opt/libffi/lib/pkgconfig
<span style="color: #767676;"># </span><span style="color: #767676;">Tell gtk that it's quartz</span>
stack install --flag gtk:have-quartz-gtk
</pre>
</div>
</div>
</li>
</ol>
</div>
</div>
<div id="outline-container-org29dd1b4" class="outline-3">
<h3 id="org29dd1b4"><span class="section-number-3">6.3.</span> Docker</h3>
<div class="outline-text-3" id="text-6-3">
<p>
Different computers have different setups, operating systems, and repositories.
Do put the entire program in a container to bypass difficulties (with the other
methods above), we user <code>docker</code>. So first, <a href="https://docs.docker.com/">install docker</a>.
</p>
<p>
To get <code>too-many-cells</code> (replace 2.0.0.0 with <a href="https://cloud.docker.com/repository/docker/gregoryschwartz/too-many-cells/general">any version needed</a>):
</p>
<div class="org-src-container">
<pre class="src src-sh">docker pull gregoryschwartz/too-many-cells:2.0.0.0
</pre>
</div>
<p>
To run <code>too-many-cells</code> in a docker container:
</p>
<div class="org-src-container">
<pre class="src src-sh">sudo docker run -it --rm -v <span style="color: #afaf00;">"/home/username:/home/username"</span> gregoryschwartz/too-many-cells:2.0.0.0 -h
</pre>
</div>
<p>
Now you can follow the tutorial below with the addition of the docker paths and
commands. If you add yourself to the docker group, <code>sudo</code> is not needed. For instance:
</p>
<div class="org-src-container">
<pre class="src src-sh">docker run -it --rm -v <span style="color: #afaf00;">"/home/username:/home/username"</span> <span style="color: #afaf00;">\</span>
gregoryschwartz/too-many-cells:2.0.0.0 make-tree <span style="color: #afaf00;">\</span>
--matrix-path /home/username/path/to/input <span style="color: #afaf00;">\</span>
--labels-file /home/username/path/to/labels.csv <span style="color: #afaf00;">\</span>
--draw-collection <span style="color: #afaf00;">"PieRing"</span> <span style="color: #afaf00;">\</span>
--output /home/username/path/to/out <span style="color: #afaf00;">\</span>
> clusters.csv
</pre>
</div>
<p>
Make sure to <a href="https://docs.docker.com/config/containers/resource_constraints/">increase the memory</a> that can be used by docker containers if you
use macOS or Windows. Also, docker won't be able to find your files by default.
You need to mount the folders with <code>-v</code> in order to have docker read and write
from and to the filesystem, respectively. Read the <a href="https://docs.docker.com/storage/volumes/">documentation</a> about volumes
for more information. You can simply mount your entire relevant path as in the
above example to handle both input and output, or just mount your entire user
directory as above. Specifically, <code>-v "/home/username:/home/username"</code> for the
whole directory or each individual <code>-v /path/to/matrix/on/host:/input_matrix</code>
with <code>-m /input_matrix</code> is what you want, where before the <code>:</code> is on the host
filesystem while after the <code>:</code> is what the docker program sees. Then you can
write the output in the same way: <code>-v /path/to/output/on/host:/output</code> will
write the output to the folder before the <code>:</code>.
</p>
<p>
To build the <code>too-many-cells</code> image yourself if you want:
</p>
<div class="org-src-container">
<pre class="src src-sh">nix-build docker.nix
docker load < /nix/store/$<span style="color: #008787;">{</span><span style="color: #87afaf;">NAME_OF_OUTPUT_IMAGE</span><span style="color: #008787;">}</span>.tar.gz
</pre>
</div>
</div>
</div>
</div>
<div id="outline-container-org38b35bd" class="outline-2">
<h2 id="org38b35bd"><span class="section-number-2">7.</span> Troubleshooting</h2>
<div class="outline-text-2" id="text-7">
</div>
<div id="outline-container-org31b88a0" class="outline-3">
<h3 id="org31b88a0"><span class="section-number-3">7.1.</span> Using nix, I'm getting shared object not found errors.</h3>
<div class="outline-text-3" id="text-7-1">
<p>
Be sure to have <code>LD_LIBRARY_PATH</code> unset when running <code>nix-env</code> to make sure the
linked libraries are in <code>/nix/store</code>.
</p>
</div>
</div>
<div id="outline-container-orgd655944" class="outline-3">
<h3 id="orgd655944"><span class="section-number-3">7.2.</span> I am getting errors like <code>AesonException "Error in $.packages.cassava.constraints.flags...</code> when running <code>stack</code> commands</h3>
<div class="outline-text-3" id="text-7-2">
<p>
Try upgrading stack with <code>stack upgrade</code>. The new installation will be in
<code>~/.local/bin</code>, so use that binary.
</p>
</div>
</div>
<div id="outline-container-org74de1e9" class="outline-3">
<h3 id="org74de1e9"><span class="section-number-3">7.3.</span> I use conda or custom ld library locations and I cannot install <code>too-many-cells</code> or run into weird R errors</h3>
<div class="outline-text-3" id="text-7-3">
<p>
<code>stack</code> and <code>too-many-cells</code> assume system libraries and programs. To solve this
issue, first install the dependencies above at the system level, including
system <code>R</code>. Then to every <code>stack</code> and <code>too-many-cells</code> command, prepend
<code>PATH="$HOME/.local/bin:/usr/bin:$PATH"</code> to all commands. For instance:
</p>
<ul class="org-ul">
<li><code>PATH="$HOME/.local/bin:/usr/bin:$PATH" stack install</code></li>
<li><code>PATH="$HOME/.local/bin:/usr/bin:$PATH" too-many-cells make-tree -h</code></li>
</ul>
<p>
If your shared libraries are abnormal and use <code>libR.so</code> from non-system
locations, be sure to also have <code>LD_LIBRARY_PATH=/usr/lib/:$LD_LIBRARY_PATH</code>
when installing (and / or the location of R libraries, such as
<code>/usr/local/lib/R/lib/</code>).
</p>
</div>
</div>
<div id="outline-container-org2478aec" class="outline-3">
<h3 id="org2478aec"><span class="section-number-3">7.4.</span> I am still having issues with installation</h3>
<div class="outline-text-3" id="text-7-4">
<p>
<a href="https://github.com/GregorySchwartz/too-many-cells/issues">Open an issue</a>! While working on the issue, try out the docker for
<code>too-many-cells</code>, it requires no installation at all (other than docker).
</p>
</div>
</div>
<div id="outline-container-orgdf2bfb6" class="outline-3">
<h3 id="orgdf2bfb6"><span class="section-number-3">7.5.</span> I am on macOS/Windows with docker and <code>too-many-cells</code> silently crashes.</h3>
<div class="outline-text-3" id="text-7-5">
<p>
Docker containers may run into this issue if the memory given to the containers
is insufficient. Make sure to <a href="https://docs.docker.com/config/containers/resource_constraints/">increase the memory</a> that can be used by docker
containers.
</p>
</div>
</div>
<div id="outline-container-org2801396" class="outline-3">
<h3 id="org2801396"><span class="section-number-3">7.6.</span> I am getting the error <code>--draw-leaf</code> cannot be read, but I copied the command!</h3>
<div class="outline-text-3" id="text-7-6">
<p>
For some computers, you may need to change the command to single quotations for
the argument: <code>--draw-leaf 'DrawItem (DrawContinuous [\"Cd4\"])'</code>
</p>
</div>
</div>
</div>
<div id="outline-container-org4549d0d" class="outline-2">
<h2 id="org4549d0d"><span class="section-number-2">8.</span> Included projects</h2>
<div class="outline-text-2" id="text-8">
<p>
This project is a collection of libraries and programs written specifically for
<code>too-many-cells</code>:
</p>
<dl class="org-dl">
<dt><a href="https://github.com/GregorySchwartz/birch-beer"> <code>birch-beer</code> </a></dt><dd>Generate a tree for displaying a hierarchy of groups with
colors, scaling, and more.</dd>
<dt><a href="https://github.com/GregorySchwartz/modularity"> <code>modularity</code> </a></dt><dd>Find the modularity of a network.</dd>
<dt><a href="https://github.com/GregorySchwartz/spectral-clustering"> <code>spectral-clustering</code> </a></dt><dd>Library for spectral clustering.</dd>
<dt><a href="https://github.com/GregorySchwartz/hierarchical-spectral-clustering"> <code>hierarchical-spectral-clustering</code> </a></dt><dd>Hierarchical spectral clustering of a
graph.</dd>
<dt><a href="https://github.com/GregorySchwartz/differential"> <code>differential</code> </a></dt><dd>Finds out whether an entity comes from different
distributions (statuses).</dd>
</dl>
</div>
</div>
<div id="outline-container-org4f4fa59" class="outline-2">
<h2 id="org4f4fa59"><span class="section-number-2">9.</span> Usage</h2>
<div class="outline-text-2" id="text-9">
<p>
<code>too-many-cells</code> has several entry points depending on the desired analysis.
</p>
<table border="2" cellspacing="0" cellpadding="6" rules="groups" frame="hsides">
<colgroup>
<col class="org-left" />
<col class="org-left" />
</colgroup>
<thead>
<tr>
<th scope="col" class="org-left">Argument</th>
<th scope="col" class="org-left">Analysis</th>
</tr>
</thead>
<tbody>
<tr>
<td class="org-left"><code>make-tree</code></td>
<td class="org-left">Generate the tree from single cell data with various measurement outputs and visualize tree</td>
</tr>
<tr>
<td class="org-left"><code>interactive</code></td>
<td class="org-left">Interactive visuzalization of the tree, very slow</td>
</tr>
<tr>
<td class="org-left"><code>differential</code></td>
<td class="org-left">Find differentially expressed features between two nodes</td>
</tr>
<tr>
<td class="org-left"><code>diversity</code></td>
<td class="org-left">Conduct diversity analyses of multiple cell populations</td>
</tr>
<tr>
<td class="org-left"><code>paths</code></td>
<td class="org-left">The binary tree equivalent of the so called "pseudotime", or 1D dimensionality reduction</td>
</tr>
</tbody>
</table>
<p>
The main workflow is to first generate and plot the population tree using
<code>too-many-cells make-tree</code>, then use the rest of the entry points as needed.
</p>
<p>
At any point, use <code>-h</code> to see the help of each entry point.
</p>
<p>
Also, check out <a href="https://github.com/GregorySchwartz/tooManyCellsR">tooManyCellsR</a> for an R wrapper!
</p>
</div>
<div id="outline-container-makeTreeUsage" class="outline-3">
<h3 id="makeTreeUsage"><span class="section-number-3">9.1.</span> <code>make-tree</code></h3>
<div class="outline-text-3" id="text-makeTreeUsage">
<p>
<code>too-many-cells make-tree</code> generates a binary tree using hierarchical spectral
clustering. We start with all cells in a single node. Spectral clustering
partitions the cells into two groups. We assess the clustering using
Newman-Girvan modularity: if \(Q > 0\) then we recursively continue with
hierarchical spectral clustering. If not, then there is only a single community
and we do not partition – the resulting node is a leaf and is considered the
finest-grain cluster.
</p>
<p>
<b>The most important argument is the –prior argument.</b> Making the tree may
take some time, so if the tree was already generated and other analysis or
visualizations need to be run on the tree, point the <code>--prior</code> argument to the
output folder from a previous run of <code>too-many-cells</code>. If you do not use
<code>--prior</code>, <b>the entire tree will be recalculated even if you just wanted to
change the visualization!</b>
</p>
<p>
The main input is the <code>--matrix-path</code> argument. When a directory is supplied,
<code>too-many-cells</code> interprets the folder to have <code>matrix.mtx</code>, <code>genes.tsv</code>, and
<code>barcodes.tsv</code> files (<code>cellranger</code> outputs, see <code>cellranger</code> for specifics). If
a file is supplied instead of a directory, we assume a <code>csv</code> file containing
feature row names and cell column names. This argument can be called multiple times
to combine multiple single cell matrices: <code>--matrix-path input1 --matrix-path
input2</code>.
</p>
<p>
The second most important argument is <code>--labels-file</code>. Supply with a <code>csv</code> with
a format and header of "item,label" to provide colorings and statistics of the
relationships between labels. Here the "item" column contains the name of each
cell (barcode) and the label is any property of the cell (the tissue of origin,
hour in a time course, celltype, etc.). You can also now use <code>-Z</code> as a list for
each matching <code>-m</code> in order to manually give the entire matrix that label
(useful for situations like <code>-m ./t-all -Z T-ALL -m ./control -Z Control). To
get the newly generated labels with =-Z</code> into a <code>labels.csv</code> file, specify
<code>--labels-output</code> and the <code>labels.csv</code> will be in the output folder.
</p>
<p>
To see the full list of options, use <code>too-many-cells -h</code> and <code>-h</code> for each entry
point (i.e. <code>too-many-cells make-tree -h</code>).
</p>
</div>
<div id="outline-container-orgeb67830" class="outline-4">
<h4 id="orgeb67830"><span class="section-number-4">9.1.1.</span> Output</h4>
<div class="outline-text-4" id="text-9-1-1">
<p>
<code>too-many-cells make-tree</code> generates several files in the output folder. Below
is a short description of each file.
</p>
<table border="2" cellspacing="0" cellpadding="6" rules="groups" frame="hsides">
<colgroup>
<col class="org-left" />
<col class="org-left" />
</colgroup>
<thead>
<tr>
<th scope="col" class="org-left">File</th>
<th scope="col" class="org-left">Description</th>
</tr>
</thead>
<tbody>
<tr>
<td class="org-left"><code>clumpiness.csv</code></td>
<td class="org-left">When labels are provided, uses the clumpiness measure to determine the level of aggregation between each label within the tree.</td>
</tr>
<tr>
<td class="org-left"><code>clumpiness.pdf</code></td>
<td class="org-left">When labels are provided, a figure of the clumpiness between labels.</td>
</tr>
<tr>
<td class="org-left"><code>cluster_diversity.csv</code></td>
<td class="org-left">When labels are provided, the diversity, or "effective number of labels", of each cluster.</td>
</tr>
<tr>
<td class="org-left"><code>cluster_info.csv</code></td>
<td class="org-left">Various bits of information for each cluster and the path leading up to each cluster, from that cluster to the root. For instance, the <code>size</code> column has <code>cluster_size/parent_size/parent_parent_size/.../root_size</code></td>
</tr>
<tr>
<td class="org-left"><code>cluster_list.json</code></td>
<td class="org-left">The <code>json</code> file containing a list of clusterings.</td>
</tr>
<tr>
<td class="org-left"><code>cluster_tree.json</code></td>
<td class="org-left">The <code>json</code> file containing the output tree in a recursive format.</td>
</tr>
<tr>
<td class="org-left"><code>dendrogram.svg</code></td>
<td class="org-left">The visualization of the tree. There are many possible options for this visualization included. Can rename to choose between PNG, PS, PDF, and SVG using <code>--dendrogram-output</code>.</td>
</tr>
<tr>
<td class="org-left"><code>graph.dot</code></td>
<td class="org-left">A <code>dot</code> file of the tree, with less information than the tree in <code>cluster_results.json</code>.</td>
</tr>
<tr>
<td class="org-left"><code>node_info.csv</code></td>
<td class="org-left">Various information of each node in the tree.</td>
</tr>
<tr>
<td class="org-left"><code>projection.pdf</code></td>
<td class="org-left">When <code>--projection</code> is supplied with a file of the format "barcode,x,y", provides a plot of each cell at the specified x and y coordinates (for instance, when looking at t-SNE plots with the same labelings as the dendrogram here).</td>
</tr>
</tbody>
</table>
</div>
</div>
<div id="outline-container-orgb2d1b3c" class="outline-4">
<h4 id="orgb2d1b3c"><span class="section-number-4">9.1.2.</span> Outline with options</h4>
<div class="outline-text-4" id="text-9-1-2">
<p>
The basic outline of the <b>default</b> matrix pre-processing pipeline with some
relevant options is as follows (there are many additional options including cell
whitelists that can be seen using <code>too-many-cells make-tree -h</code>):
</p>
<ol class="org-ol">
<li>Read matrix.</li>
<li>Optionally remove cells with less than X counts (<code>--filter-thresholds</code>).</li>
<li>Optionally remove features with less than X count (<code>--filter-thresholds</code>).</li>
<li>Term frequency-inverse document frequency normalization (<code>--normalization</code>).</li>
<li>Optionally use dimensionality reduction (<code>--lsa</code>).</li>
<li>Finish.</li>
</ol>
</div>
</div>
<div id="outline-container-org05486bc" class="outline-4">
<h4 id="org05486bc"><span class="section-number-4">9.1.3.</span> Example</h4>
<div class="outline-text-4" id="text-9-1-3">
</div>
<ol class="org-ol">
<li><a id="preprocessedData"></a>Setup<br />
<div class="outline-text-5" id="text-preprocessedData">
<p>
We start with our input matrix. Here,
</p>
<div class="org-src-container">
<pre class="src src-sh">ls ./input
</pre>
</div>
<pre class="example" id="org121b64d">
barcodes.tsv genes.tsv matrix.mtx
</pre>
<p>
Note that the input can be a directory (with the <code>cellranger</code> matrix format
above) or a file (a <code>csv</code> file). You can also point to a <code>cellranger</code> >= 3.0
folder which has <code>matrix.mtx.gz</code>, <code>features.tsv.gz</code>, and <code>barcodes.tsv.gz</code> files
instead. <b>You don't need to use scRNA-seq data!</b> You can use any data that has
observations (cells) and features (genes), as long as you agree that the
observations are related by their feature abundances. If
you do upstream batch effect correction, LSA, normalization, or anything else,
be sure to use <code>--normalization NoneNorm</code> (and <code>--shift-positive</code>
for LSA) to avoid wrong filters and scalings! <b>If using dimensionality reduction
such as PCA and t-SNE</b>, we highly recommend generating your own similarity
matrix for use with our <code>cluster-tree</code> program and plot with <code>birch-beer</code>, as we
emphasize a feature matrix in <code>too-many-cells</code> and dimensionality reduction
algorithms transform counts (our input which works with cosine similarity) into
more nebulous information (which may not work with cosine similarity).
<code>cluster-tree</code>, however, can be used with adjacency and similarity matrices. As
for formats, the matrix market format contains three files like so:
</p>
<p>
The <code>matrix.mtx</code> file is in matrix market format.
</p>
<pre class="example" id="org3bf76ae">
%%MatrixMarket matrix coordinate integer general
%
23433 1981 4255069
4 1 1
5 1 1
11 1 2
23 1 2
25 1 2
40 1 2
48 1 1
...
</pre>
<p>
The <code>genes.tsv</code> file (or <code>features.tsv.gz</code>) contains the features of each cell
and corresponds to the rows of <code>matrix.mtx</code>. Here, both columns were the same
gene symbols, but you can have Ensembl as the first column and gene symbol as
the second, etc. The columns and column orders don't matter, but make sure all
matrices have the same format and specify the symbols you want to use (for
overlaying gene expression, differential expression, etc.) with
<code>--feature-column COLUMN</code>. So to use the second column for gene expression, you
would use <code>--feature-column 2</code>.
</p>
<pre class="example" id="org857ab38">
Xkr4 Xkr4
Rp1 Rp1
Sox17 Sox17
Mrpl15 Mrpl15
Lypla1 Lypla1
Tcea1 Tcea1
Rgs20 Rgs20
Atp6v1h Atp6v1h
Oprk1 Oprk1
Npbwr1 Npbwr1
...
</pre>
<p>
The <code>barcodes.tsv</code> file contains the ids of each cell or observation and
corresponds to the columns of <code>matrix.mtx</code>.
</p>
<pre class="example" id="org7a9ca19">
AAACCTGCAGTAACGG-1
AAACGGGAGAAGAAGC-1
AAACGGGAGACCGGAT-1
AAACGGGAGCGCTCCA-1
AAACGGGAGGACGAAA-1
AAACGGGAGGTACTCT-1
AAACGGGAGGTGCTTT-1
AAACGGGAGTCGAGTG-1
AAACGGGCATGGTCAT-1
AAAGATGAGCTTCGCG-1
...
</pre>
<p>
For a <code>csv</code> file, the format is dense (observation columns (cells), feature rows
(genes)):
</p>
<pre class="example" id="org37c47fd">
"","A22.D042044.3_9_M.1.1","C5.D042044.3_9_M.1.1","D10.D042044.3_9_M.1.1","E13.D042044.3_9_M.1.1","F19.D042044.3_9_M.1.1","H2.D042044.3_9_M.1.1","I9.D042044.3_9_M.1.1",...
"0610005C13Rik",0,0,0,0,0,0,0,...
"0610007C21Rik",0,112,185,54,0,96,42,...
"0610007L01Rik",0,0,0,0,0,153,170,...
"0610007N19Rik",0,0,0,0,0,0,0,...
"0610007P08Rik",0,0,0,0,0,19,0,...
"0610007P14Rik",0,58,0,0,255,60,0,...
"0610007P22Rik",0,0,0,0,0,65,0,...
"0610008F07Rik",0,0,0,0,0,0,0,...
"0610009B14Rik",0,0,0,0,0,0,0,...
...
</pre>
<p>
We also know where each cell came from, so we mark that down as well in a
<code>labels.csv</code> file.
</p>
<pre class="example" id="orgaaa9c94">
item,label
AAACCTGCAGTAACGG-1,Marrow
AAACGGGAGACCGGAT-1,Marrow
AAACGGGAGCGCTCCA-1,Marrow
AAACGGGAGGACGAAA-1,Marrow
AAACGGGAGGTACTCT-1,Marrow
...
</pre>
<p>
This can be easily accomplished with <code>sed</code>:
</p>
<div class="org-src-container">
<pre class="src src-sh">cat barcodes.tsv | sed <span style="color: #afaf00;">"s/-1/-1,Marrow/"</span> | s/-2/etc... > labels.csv
</pre>
</div>
<p>
For <code>cellranger</code>, note that the <code>-1</code>, <code>-2</code>, etc. postfixes denote the first,
second, etc. label in the aggregation <code>csv</code> file used as input for <code>cellranger
aggr</code>.
</p>
</div>
</li>
<li><a id="orga249f36"></a>Default run<br />
<div class="outline-text-5" id="text-9-1-3-2">
<p>
We can now run the <code>too-many-cells</code> algorithm on our data. The resulting cells
with assigned clusters will be printed to <code>stdout</code> (don't forget to use
<code>--normalization NoneNorm</code> on preprocessed data, as stated <a href="#preprocessedData">here</a>). While older
versions had default filter thresholds for (MINCELL, MINFEATURE) counts, since
<code>v2.0.0.0</code> the default is now no filtering to account for multiple assay types.
</p>
<div class="org-src-container">
<pre class="src src-sh">too-many-cells make-tree <span style="color: #afaf00;">\</span>
--matrix-path input <span style="color: #afaf00;">\</span>
--labels-file labels.csv <span style="color: #afaf00;">\</span>
--filter-thresholds <span style="color: #afaf00;">"(250, 1)"</span> <span style="color: #afaf00;">\</span>
--draw-collection <span style="color: #afaf00;">"PieRing"</span> <span style="color: #afaf00;">\</span>
--output out <span style="color: #afaf00;">\</span>
> clusters.csv
</pre>
</div>
<div id="org12e536b" class="figure">
<p><img src="img/complete_default_tree.png" alt="complete_default_tree.png" />
</p>
</div>
</div>
</li>
<li><a id="org12e497f"></a>Pruning tree<br />
<div class="outline-text-5" id="text-9-1-3-3">
<p>
Large cell populations can result in a very large tree. What if we only want to
see larger subpopulations rather than the large (inner nodes) and small
(leaves)? We can use the <code>--min-size 100</code> argument to set the minimum size of a
leaf to 100 in this case. Alternatively, we can specify <code>--smart-cutoff 4</code> in
addition to <code>--min-size 1</code> to set the minimum size of a node to \(4 *
\text{median absolute deviation (MAD)}\) of the nodes in the original tree.
Varying the number of MADs varies the number of leaves in the tree.
<code>--smart-cutoff</code> should be used in addition to <code>--min-size</code>, <code>--max-proportion</code>,
<code>--min-distance</code>, or <code>--min-distance-search</code> to decide which cutoff variable to
use. The value supplied to the cutoff variable is ignored when <code>--smart-cutoff</code>
is specified. We'll prune the tree for better visibility in this document.
</p>
<p>
<b>Note: the pruning arguments change the tree file, not just the plot, so be sure
to output into a different directory.</b>
</p>
<p>
Also, <b>we do not need to recalculate the entire tree!</b> We can just supply the
previous results using <code>--prior</code> (we can also remove <code>--matrix-path</code> with
<code>--prior</code> to speed things up, but miss out on some features if needed):
</p>
<div class="org-src-container">
<pre class="src src-sh">too-many-cells make-tree <span style="color: #afaf00;">\</span>
--prior out <span style="color: #afaf00;">\</span>
--labels-file labels.csv <span style="color: #afaf00;">\</span>
--smart-cutoff <span style="color: #d787af;">4</span> <span style="color: #afaf00;">\</span>
--min-size <span style="color: #d787af;">1</span> <span style="color: #afaf00;">\</span>
--draw-collection <span style="color: #afaf00;">"PieRing"</span> <span style="color: #afaf00;">\</span>
--output out_pruned <span style="color: #afaf00;">\</span>
> clusters_pruned.csv