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Code Issues Packages Projects Releases Wiki Activity

Add user-defined category-label colors (#1402)

Browse Source 
* Add user-defined category-label colors

Fixes https://github.com/chanzuckerberg/cellxgene/issues/1152

As described in https://github.com/chanzuckerberg/cellxgene/issues/1307

* Respond to feedback from @bkmartinjr in nodejs

* Respond to feedback from @bkmartinjr in python

* Add tests to the server module

* Autoformat python, run linter

* Make colors_get error handling specific

* Respond to feedback from @bkmartinjr

* Respond to feedback from @bkmartinjr

* Fix whitespace

* Fix python lint errrors

* Update documentation

* Add --disable-user-colors option to launch and cxgtool.py

* Fix python formatting

* Rename '--disable-user-colors' to '--disable-custom-colors'
This commit is contained in:
Matt Weiden
2020-04-26 22:52:57 -07:00
committed by GitHub
parent 3c191c3f93
commit 546e272a60
91 changed files with 801 additions and 271 deletions
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@@ -102,8 +102,8 @@
<ul>
<li>Expression values (raw or normalized) in <code class="language-plaintext highlighter-rouge">anndata.X</code></li>
<li>At least one embedding (e.g., tSNE, UMAP) in <code class="language-plaintext highlighter-rouge">anndata.obsm</code>, specified with the prefix <code class="language-plaintext highlighter-rouge">X_</code> (e.g., by default scanpy stores UMAP coordinates in <code class="language-plaintext highlighter-rouge">anndata.obsm['X_umap']</code>)</li>
<li>A unique identifier for every cell is available in an <code class="language-plaintext highlighter-rouge">anndata.obs</code> field (you can specify this with the <code class="language-plaintext highlighter-rouge">--obs-names</code> option)</li>
<li>A unique identifier for every gene is available in an <code class="language-plaintext highlighter-rouge">anndata.var</code> field (you can specify which field to use with the <code class="language-plaintext highlighter-rouge">--var-names</code> option)</li>
<li>A unique identifier is required for each cell, which by default will be pulled from the <code class="language-plaintext highlighter-rouge">obs</code> DataFrame index. If the index is not unique or does not contain the cell ID, an alternative column can be specified with <code class="language-plaintext highlighter-rouge">--obs-names</code></li>
<li>A unique identifier is required for each gene, which by default will be pulled from the <code class="language-plaintext highlighter-rouge">var</code> DataFrame index. If the index is not unique or does not contain the cell ID, an alternative column can be specified with <code class="language-plaintext highlighter-rouge">--var-names</code></li>
</ul>
<h4 id="what-about-r-objects-from-seurat--bioconductor">What about R objects from seurat / bioconductor!?</h4>
@@ -112,6 +112,32 @@
<h4 id="can-i-use-data-hosted-on-the-web-somewhere">Can I use data hosted on the web somewhere?</h4>
<p>Yes! You can launch from a URL instead of a filepath. The same data format requirements apply. Please see <a href="launch">here</a> for more details.</p>
<h1 id="data-format-options">Data format options</h1>
<h4 id="category-colors">Category colors</h4>
<p><code class="language-plaintext highlighter-rouge">cellxgene</code> will display <a href="https://github.com/chanzuckerberg/cellxgene/issues/1152#issue-564361541">scanpy-style color
information</a>
for category-label pairs. An example of this format is shown below:</p>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>&gt;&gt;&gt; category = "louvain"
&gt;&gt;&gt; # colors stored in adata.uns must be matplotlib-compatible color information
&gt;&gt;&gt; adata.uns[f"{category}_colors"]
array(['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#bcbd22'], dtype='&lt;U7')
&gt;&gt;&gt; # there must be a matching category in adata.obs
&gt;&gt;&gt; category in adata.obs
True
</code></pre></div></div>
<p>To test that you’ve done this properly, check that for your given <code class="language-plaintext highlighter-rouge">category</code> the number of colors match the number of category values and that the second command below results in a mapping from categories to colors.</p>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>&gt;&gt;&gt; len(adata.obs[category].cat.categories) == len(adata.uns[f"{category}_colors"])
True
&gt;&gt;&gt; dict(zip(adata.obs[category].cat.categories, adata.uns[f"{category}_colors"]))
{'CD4 T cells': '#1f77b4', 'CD14+ Monocytes': '#ff7f0e', 'B cells': '#2ca02c', 'CD8 T cells': '#d62728', 'NK cells': '#9467bd', 'FCGR3A+ Monocytes': '#8c564b', 'Dendritic cells': '#e377c2', 'Megakaryocytes': '#bcbd22'}
</code></pre></div></div>
<p>You can disable this feature using the <code class="language-plaintext highlighter-rouge">--disable-custom-colors</code> flag for <code class="language-plaintext highlighter-rouge">cellxgene launch</code>. cellxgene will then chose colors from its standard color palettes.</p>
<h1 id="using-cellxgene-prepare">Using <code class="language-plaintext highlighter-rouge">cellxgene prepare</code></h1>
<p>If your data is in a different format, and/or you still need to perform dimensionality reduction and/or clustering, <code class="language-plaintext highlighter-rouge">cellxgene</code> can do that for you with the <code class="language-plaintext highlighter-rouge">prepare</code> command.</p>
@@ -136,7 +162,7 @@
<p><code class="language-plaintext highlighter-rouge">cellxgene prepare</code> is not meant as a way to formally process or analyze your data. It’s simply a utility for quickly wrangling your data into cellxgene-compatible format and computing a “vanilla” embedding so you can try out <code class="language-plaintext highlighter-rouge">cellxgene</code> and get a general sense of a dataset.</p>
<h2 id="quickstart-for-cellxgene-prepare">Quickstart for <code class="language-plaintext highlighter-rouge">cellxgene prepare</code></h2>
<p>To add <code class="language-plaintext highlighter-rouge">cellxgene prepare</code> to your <a href="install">cellxgene installation</a>, run<br />
<p>To add <code class="language-plaintext highlighter-rouge">cellxgene prepare</code> to your <a href="install">cellxgene installation</a>, run
<code class="language-plaintext highlighter-rouge">pip install cellxgene[prepare]</code></p>
<p>Then run <code class="language-plaintext highlighter-rouge">prepare</code> on your data with:</p>
@@ -162,36 +188,36 @@
<p>Let’s look at what <code class="language-plaintext highlighter-rouge">prepare</code> is doing to our data, and how each step relates to the command above. You can see a walkthrough of what’s going on under the hood for this example in <a href="https://github.com/chanzuckerberg/cellxgene-vignettes/blob/master/dataset-processing/pbmc3k-prepare-example.ipynb">this notebook</a>.</p>
<p><strong>(A) - Compute quality control metrics and store this in our <code class="language-plaintext highlighter-rouge">AnnData</code> object for later inspection</strong> <br />
<strong>(B) - Normalize the expression matrix using a basic preprocessing recipe</strong> <br />
<strong>(auto) - Do some preprocessing to run PCA and compute the neighbor graph</strong><br />
<strong>(auto) - Infer clusters with the Louvain algorithm and store these labels to visualize later</strong><br />
<strong>(C) - Compute and store UMAP and tSNE embeddings</strong><br />
<p><strong>(A) - Compute quality control metrics and store this in our <code class="language-plaintext highlighter-rouge">AnnData</code> object for later inspection</strong>
<strong>(B) - Normalize the expression matrix using a basic preprocessing recipe</strong>
<strong>(auto) - Do some preprocessing to run PCA and compute the neighbor graph</strong>
<strong>(auto) - Infer clusters with the Louvain algorithm and store these labels to visualize later</strong>
<strong>(C) - Compute and store UMAP and tSNE embeddings</strong>
<strong>(D) - Write results to file</strong></p>
<h2 id="options-for-cellxgene-prepare">Options for cellxgene <code class="language-plaintext highlighter-rouge">prepare</code></h2>
<p><strong>For the most up-to-date and comprehensive list of options, run <code class="language-plaintext highlighter-rouge">cellxgene prepare --help</code></strong></p>
<p><code class="language-plaintext highlighter-rouge">--embedding</code> controls which dimensionality reduction algorithm is applies to your data.<br />
<p><code class="language-plaintext highlighter-rouge">--embedding</code> controls which dimensionality reduction algorithm is applies to your data.
Options are <code class="language-plaintext highlighter-rouge">umap</code> and/or <code class="language-plaintext highlighter-rouge">tsne</code>. Defaults to both.</p>
<p><code class="language-plaintext highlighter-rouge">--recipe</code> controls which normalization steps to apply to your data, based on one of the preprocessing <code class="language-plaintext highlighter-rouge">recipes</code> included with <code class="language-plaintext highlighter-rouge">scanpy</code>.
These recipes include steps like cell filtering and gene selection; see the <code class="language-plaintext highlighter-rouge">scanpy</code> <a href="https://scanpy.readthedocs.io/en/latest/api/index.html#recipes">documentation</a> for more details. <br />
These recipes include steps like cell filtering and gene selection; see the <code class="language-plaintext highlighter-rouge">scanpy</code> <a href="https://scanpy.readthedocs.io/en/latest/api/index.html#recipes">documentation</a> for more details.
Options are <code class="language-plaintext highlighter-rouge">none</code>, <code class="language-plaintext highlighter-rouge">seurat</code>, or <code class="language-plaintext highlighter-rouge">zheng17</code>. Defaults to <code class="language-plaintext highlighter-rouge">none</code>.</p>
<p><code class="language-plaintext highlighter-rouge">--sparse</code> is a flag determines whether to enforce a sparse matrix. For large datasets, <code class="language-plaintext highlighter-rouge">prepare</code> can take a long time to run (a few minutes for datasets with 10-100k cells, up to an hour or more for datasets with &gt;100k cells). If you want <code class="language-plaintext highlighter-rouge">prepare</code> to run faster we recommend using the <code class="language-plaintext highlighter-rouge">sparse</code> option.<br />
<p><code class="language-plaintext highlighter-rouge">--sparse</code> is a flag determines whether to enforce a sparse matrix. For large datasets, <code class="language-plaintext highlighter-rouge">prepare</code> can take a long time to run (a few minutes for datasets with 10-100k cells, up to an hour or more for datasets with &gt;100k cells). If you want <code class="language-plaintext highlighter-rouge">prepare</code> to run faster we recommend using the <code class="language-plaintext highlighter-rouge">sparse</code> option.
If this flag is not included, default is <code class="language-plaintext highlighter-rouge">False</code></p>
<p><code class="language-plaintext highlighter-rouge">--skip-qc</code> by default, <code class="language-plaintext highlighter-rouge">cellxgene prepare</code> will compute quality control metrics (saved to <code class="language-plaintext highlighter-rouge">anndata.obs</code> and <code class="language-plaintext highlighter-rouge">anndata.var</code>) as described in the <code class="language-plaintext highlighter-rouge">scanpy</code> <a href="https://scanpy.readthedocs.io/en/stable/api/scanpy.pp.calculate_qc_metrics.html">documentation</a>. Pass this flag if you would like to skip this step.</p>
<p><code class="language-plaintext highlighter-rouge">--make-obs-names-unique</code> / <code class="language-plaintext highlighter-rouge">--make-var-names-unique</code> determine whether to rename <code class="language-plaintext highlighter-rouge">obs</code> (cell) / <code class="language-plaintext highlighter-rouge">var</code> (gene) names, respectively, to be unique.<br />
<p><code class="language-plaintext highlighter-rouge">--make-obs-names-unique</code> / <code class="language-plaintext highlighter-rouge">--make-var-names-unique</code> determine whether to rename <code class="language-plaintext highlighter-rouge">obs</code> (cell) / <code class="language-plaintext highlighter-rouge">var</code> (gene) names, respectively, to be unique.
Default is <code class="language-plaintext highlighter-rouge">True</code>.</p>
<p><code class="language-plaintext highlighter-rouge">--set-obs-names</code> controls which field in <code class="language-plaintext highlighter-rouge">anndata.obs</code> (cell metadata) is used as the <em>index</em> for cells (e.g., a cell ID column).<br />
<p><code class="language-plaintext highlighter-rouge">--set-obs-names</code> controls which field in <code class="language-plaintext highlighter-rouge">anndata.obs</code> (cell metadata) is used as the <em>index</em> for cells (e.g., a cell ID column).
Default is <code class="language-plaintext highlighter-rouge">anndata.obs.names</code></p>
<p><code class="language-plaintext highlighter-rouge">--set-var-names</code> controls which field in <code class="language-plaintext highlighter-rouge">anndata.var</code> (gene metadata) is used as the <em>index</em> for genes.<br />
<p><code class="language-plaintext highlighter-rouge">--set-var-names</code> controls which field in <code class="language-plaintext highlighter-rouge">anndata.var</code> (gene metadata) is used as the <em>index</em> for genes.
Default is <code class="language-plaintext highlighter-rouge">anndata.var.names</code></p>
<p><code class="language-plaintext highlighter-rouge">--output</code> and <code class="language-plaintext highlighter-rouge">--overwrite</code> control where the processed data is saved.</p>
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