Deploying to gh-pages from @ 3884ffd 🚀

_autosummary/cellxgene_census.experimental.ml.pytorch.ExperimentDataPipe.html

@@ -238,7 +238,7 @@ <h1>cellxgene_census.experimental.ml.pytorch.ExperimentDataPipe<a class="headerl
 <p>The <code class="docutils literal notranslate"><span class="pre">return_sparse_X</span></code> parameter controls whether the <code class="docutils literal notranslate"><span class="pre">X</span></code> data is returned as a dense or sparse
 <a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a>. If the model supports use of sparse <a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a>s, this will reduce memory usage.</p>
 <p>The <code class="docutils literal notranslate"><span class="pre">obs_column_names</span></code> parameter determines the data columns that are returned in the <code class="docutils literal notranslate"><span class="pre">obs</span></code> Tensor. The first
-element is always the <code class="docutils literal notranslate"><span class="pre">soma_joinid</span></code> of the <code class="docutils literal notranslate"><span class="pre">obs</span></code> <a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a> (or, equivalently, the
+element is always the <code class="docutils literal notranslate"><span class="pre">soma_joinid</span></code> of the <code class="docutils literal notranslate"><span class="pre">obs</span></code> <a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a> (or, equivalently, the
 <code class="docutils literal notranslate"><span class="pre">soma_dim_0</span></code> of the <code class="docutils literal notranslate"><span class="pre">X</span></code> matrix). The remaining elements are the <code class="docutils literal notranslate"><span class="pre">obs</span></code> columns specified by
 <code class="docutils literal notranslate"><span class="pre">obs_column_names</span></code>, and string-typed columns are encoded as integer values. If needed, these values can be decoded
 by obtaining the encoder for a given <code class="docutils literal notranslate"><span class="pre">obs</span></code> column name and calling its <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code> method:</p>

_autosummary/cellxgene_census.experimental.pp.mean_variance.html

@@ -209,7 +209,7 @@
 <h1>cellxgene_census.experimental.pp.mean_variance<a class="headerlink" href="#cellxgene-census-experimental-pp-mean-variance" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="cellxgene_census.experimental.pp.mean_variance">
-<span class="sig-prename descclassname"><span class="pre">cellxgene_census.experimental.pp.</span></span><span class="sig-name descname"><span class="pre">mean_variance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">query</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://tiledbsoma.readthedocs.io/en/latest/_autosummary/tiledbsoma.ExperimentAxisQuery.html#tiledbsoma.ExperimentAxisQuery" title="(in TileDB-SOMA-Py)"><span class="pre">ExperimentAxisQuery</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">layer</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'raw'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calculate_mean</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calculate_variance</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ddof</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nnz_only</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.1)"><span class="pre">DataFrame</span></a></span></span><a class="headerlink" href="#cellxgene_census.experimental.pp.mean_variance" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">cellxgene_census.experimental.pp.</span></span><span class="sig-name descname"><span class="pre">mean_variance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">query</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://tiledbsoma.readthedocs.io/en/latest/_autosummary/tiledbsoma.ExperimentAxisQuery.html#tiledbsoma.ExperimentAxisQuery" title="(in TileDB-SOMA-Py)"><span class="pre">ExperimentAxisQuery</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">layer</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'raw'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calculate_mean</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calculate_variance</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ddof</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nnz_only</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.2)"><span class="pre">DataFrame</span></a></span></span><a class="headerlink" href="#cellxgene_census.experimental.pp.mean_variance" title="Permalink to this definition">¶</a></dt>
 <dd><p>Calculate  mean and/or variance along the <code class="docutils literal notranslate"><span class="pre">obs</span></code> axis from query results. Calculations are done in an accumulative
 chunked fashion. For the mean and variance calculations, the total number of elements (N) is, by default, the
 corresponding dimension size: for column-wise calculations (<code class="docutils literal notranslate"><span class="pre">axis</span> <span class="pre">=</span> <span class="pre">0</span></code>) N is number of rows, for row-wise
@@ -231,7 +231,7 @@ <h1>cellxgene_census.experimental.pp.mean_variance<a class="headerlink" href="#c
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a> indexed by the <code class="docutils literal notranslate"><span class="pre">soma_joinid</span></code> and with columns <code class="docutils literal notranslate"><span class="pre">mean</span></code> (if
+<dd class="field-even"><p><a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a> indexed by the <code class="docutils literal notranslate"><span class="pre">soma_joinid</span></code> and with columns <code class="docutils literal notranslate"><span class="pre">mean</span></code> (if
 <code class="docutils literal notranslate"><span class="pre">calculate_mean</span> <span class="pre">=</span> <span class="pre">True</span></code>), and <code class="docutils literal notranslate"><span class="pre">variance</span></code> (if <code class="docutils literal notranslate"><span class="pre">calculate_variance</span> <span class="pre">=</span> <span class="pre">True</span></code>).</p>
 </dd>
 </dl>

_autosummary/cellxgene_census.get_presence_matrix.html

@@ -210,7 +210,7 @@ <h1>cellxgene_census.get_presence_matrix<a class="headerlink" href="#cellxgene-c
 <span class="sig-prename descclassname"><span class="pre">cellxgene_census.</span></span><span class="sig-name descname"><span class="pre">get_presence_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">census</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://tiledbsoma.readthedocs.io/en/latest/_autosummary/tiledbsoma.Collection.html#tiledbsoma.Collection" title="(in TileDB-SOMA-Py)"><span class="pre">Collection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">organism</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">measurement_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'RNA'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html#scipy.sparse.csr_matrix" title="(in SciPy v1.13.0)"><span class="pre">csr_matrix</span></a></span></span><a class="headerlink" href="#cellxgene_census.get_presence_matrix" title="Permalink to this definition">¶</a></dt>
 <dd><p>Read the feature dataset presence matrix and return as a <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_array.html#scipy.sparse.csr_array" title="(in SciPy v1.13.0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">scipy.sparse.csr_array</span></code></a>. The
 returned sparse matrix is indexed on the first dimension by the dataset <code class="docutils literal notranslate"><span class="pre">soma_joinid</span></code> values,
-and on the second dimension by the <code class="docutils literal notranslate"><span class="pre">var</span></code> <a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a> <code class="docutils literal notranslate"><span class="pre">soma_joinid</span></code> values.</p>
+and on the second dimension by the <code class="docutils literal notranslate"><span class="pre">var</span></code> <a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a> <code class="docutils literal notranslate"><span class="pre">soma_joinid</span></code> values.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">

_sources/articles/2023/20230808-r_api_release.md.txt

@@ -2,7 +2,7 @@
 
 *Published:* *August 7th, 2023*
 
-*By:* *[Pablo Garcia-Nieto](pgarcia-nieto@chanzuckerberg.com)*
+*By:* *[Pablo Garcia-Nieto](mailto:pgarcia-nieto@chanzuckerberg.com)*
 
 The Census team is pleased to announce the release of the R package `cellxgene.census`. 🎉 🎉
 

_sources/articles/2023/20230919-out_of_core_methods.md.txt

@@ -2,7 +2,7 @@
 
 *Published:* *September 18, 2023*
 
-*By:* *[Pablo Garcia-Nieto](pgarcia-nieto@chanzuckerberg.com)*
+*By:* *[Pablo Garcia-Nieto](mailto:pgarcia-nieto@chanzuckerberg.com)*
 
 The Census team is thrilled to officially announce memory-efficient implementations of some of the most widely used single-cell algorithms.
 

_sources/articles/2023/20231012-normalized_layer_precalc_stats.md.txt

@@ -3,7 +3,7 @@
 <!-- markdownlint-disable MD036 -->
 *Published: October 12, 2023*
 
-*By: [Maximilian Lombardo](mlombardo@chanzuckerberg.com) and [Pablo Garcia-Nieto](pgarcia-nieto@chanzuckerberg.com)*
+*By: [Maximilian Lombardo](mailto:mlombardo@chanzuckerberg.com) and [Pablo Garcia-Nieto](mailto:pgarcia-nieto@chanzuckerberg.com)*
 <!-- markdownlint-enable MD036 -->
 
 The Census team is happy to announce the introduction of two new data features, tailored to empower your single-cell research: a library-size, normalized expression layer and pre-calculated cell and gene statistics. This work is reflected in changes introduced in the [Census schema V1.1.0](https://github.com/chanzuckerberg/cellxgene-census/blob/main/docs/cellxgene_census_schema.md).

_sources/articles/2024/20240404-categoricals.md.txt

@@ -2,7 +2,7 @@
 
 *Published:* *April 4th, 2024*
 
-*By:* *[Emanuele Bezzi](ebezzi@chanzuckerberg.com)* & [Pablo Garcia-Nieto](pgarcia-nieto@chanzuckerberg.com)
+*By:* *[Emanuele Bezzi](mailto:ebezzi@chanzuckerberg.com)* & [Pablo Garcia-Nieto](mailto:pgarcia-nieto@chanzuckerberg.com)
 
 Starting with the `2024-04-01` Census build, a subset of the columns in the `obs` dataframe are now categorical instead of strings.
 

articles/2023/20230808-r_api_release.html

@@ -205,7 +205,7 @@
   <section id="r-package-cellxgene-census-v1-is-out">
 <h1>R package <code class="docutils literal notranslate"><span class="pre">cellxgene.census</span></code> V1 is out!<a class="headerlink" href="#r-package-cellxgene-census-v1-is-out" title="Permalink to this heading">¶</a></h1>
 <p><em>Published:</em> <em>August 7th, 2023</em></p>
-<p><em>By:</em> <em><a class="reference internal" href="#pgarcia-nieto&#64;chanzuckerberg.com"><span class="xref myst">Pablo Garcia-Nieto</span></a></em></p>
+<p><em>By:</em> <em><a class="reference external" href="mailto:pgarcia-nieto&#37;&#52;&#48;chanzuckerberg&#46;com">Pablo Garcia-Nieto</a></em></p>
 <p>The Census team is pleased to announce the release of the R package <code class="docutils literal notranslate"><span class="pre">cellxgene.census</span></code>. 🎉 🎉</p>
 <p>This has been long coming since our Python release back in May. Now, from R, computational biologists can access the Census data which is the largest standardized aggregation of single-cell data, composed of &gt;33M cells and &gt;60K genes.</p>
 <p>With <code class="docutils literal notranslate"><span class="pre">cellxgene.census</span></code> in a few seconds users can access any slice of Census data using cell or gene filters across hundreds of datasets. The data can be fetched in an iterative fashion for bigger-than-memory slices of data, or quickly exported to basic R structures, and <a class="reference external" href="https://satijalab.org/seurat/">Seurat</a> or <a class="reference external" href="https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html">SingleCellExperiment</a> for downstream analysis.</p>