Deploying to gh-pages from @ 7a432eb 🚀

_autosummary/cellxgene_census.experimental.pp.get_highly_variable_genes.html

@@ -269,7 +269,7 @@ <h1>cellxgene_census.experimental.pp.get_highly_variable_genes<a class="headerli
 <span class="go">    )</span>
 </pre></div>
 </div>
-<p>Fetch an <a class="reference external" href="https://anndata.readthedocs.io/en/latest/generated/anndata.AnnData.html#anndata.AnnData" title="(in anndata v0.11.1.dev38+gfaec0f8)"><code class="xref py py-class docutils literal notranslate"><span class="pre">anndata.AnnData</span></code></a> with top 500 genes:</p>
+<p>Fetch an <a class="reference external" href="https://anndata.readthedocs.io/en/latest/generated/anndata.AnnData.html#anndata.AnnData" title="(in anndata v0.12.0.dev40+g68bb5b4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">anndata.AnnData</span></code></a> with top 500 genes:</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">with</span> <span class="n">cellxgene_census</span><span class="o">.</span><span class="n">open_soma</span><span class="p">(</span><span class="n">census_version</span><span class="o">=</span><span class="s2">&quot;stable&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">census</span><span class="p">:</span>
 <span class="go">        organism = &quot;mus_musculus&quot;</span>
 <span class="go">        obs_value_filter = &quot;is_primary_data == True and tissue_general == &#39;lung&#39;&quot;</span>

_downloads/f9b6165098dce7ee3609e936dcfee0fa/census_accepted_assays.csv

@@ -96,3 +96,6 @@ EFO:0700004,BD Rhapsody Targeted mRNA
 EFO:0700010,TruDrop
 EFO:0700011,GEXSCOPE technology
 EFO:0700016,Smart-seq v4
+EFO:0010961, Visium Spatial Gene Expression
+EFO:0009920, Slide-seq
+EFO:0030062, Slide-seqV2

_sources/articles.rst.txt

@@ -18,3 +18,12 @@ What's new?
     :maxdepth: 1
 
     articles/2024/*
+
+2025
+----------
+
+.. toctree::
+    :glob:
+    :maxdepth: 1
+
+    articles/2025/*

_sources/articles/2025/20250117-spatial.md.txt

@@ -0,0 +1,53 @@
+# Beta Release of Spatial Data on Census!
+
+**Published:** Jan 14, 2025  
+**By:** Cathy Stolitzka, Isaac Virshup, Maximilian Lombardo  
+
+The Census team is pleased to announce the release of Spatial data on Census!
+
+This has been a large joint effort between the Census team and TileDB to create an easy-to-use and backwards-compatible spatial schema that enables easy analysis with non-spatial and spatial data!
+
+This first release is a **beta release** with the ability to export all spatial data (10x Visium and Slideseq) from an `obs/var` query in `SpatialData`. The ability to export `SpatialData` with spatial-based filters, transforms, etc., is not supported and will be implemented in a future release.
+
+---
+
+## SOMA Spatial Data Model
+
+![Updated CELLxGENE Census Schema with spatial data](/census-spatial-schema.svg)
+
+### Building Blocks
+
+#### **SOMAExperiment**
+
+A collection encapsulating data from one or more single-cell datasets, with reserved attributes:
+
+| Field Name             | Field Description                                                                 |
+|-------------------------|-----------------------------------------------------------------------------------|
+| `obs`                  | A DataFrame for observation metadata                                             |
+| `ms`                   | A collection (`SOMAMeasurement`), with cell-by-gene data matrices and a gene metadata DataFrame |
+| **[NEW] `spatial`**    | A collection of `Scene` objects (see below)                                      |
+| **[NEW] `obs_spatial_presence`** | A DataFrame to map observations to `Scene` objects                              |
+
+---
+
+#### **[NEW] SOMAScene**
+
+A collection of spatial assets. All assets in one `Scene` should correspond to the same physical coordinate system. The collection provides operations for getting, setting, and transforming between coordinate systems, with reserved attributes:
+
+| Field Name   | Field Description                                                                                                                                   |
+|--------------|-----------------------------------------------------------------------------------------------------------------------------------------------------|
+| `obsl`       | A collection of spatial arrays and collections. The rows in these arrays correspond to observations and may correspond to `obs` of a `SOMAExperiment`. |
+| `varl`       | A collection of collections for spatial arrays on the `SOMAMeasurements`. The top-level collection is indexed by measurement name. The rows in the contained arrays correspond to features and may correspond to `var` of the associated `SOMAMeasurement`. |
+| `img`        | A `SOMAImageCollection` of images (single and multi-resolution).                                                                                    |
+
+---
+
+#### **[NEW] SOMAImageCollection**
+
+A group of multi-resolution images that can be accessed by levels. Below are some sample operations on this collection type. Every `SOMAImageNDArray` in the collection must be mappable to the same physical space by translation and scaling only.
+
+| Operation         | Description                                                             |
+|-------------------|-------------------------------------------------------------------------|
+| `levels`          | Sequence of level numbers in the slide                                 |
+| `dimensions`      | A `(width, height)` tuple for level 0 of the slide                     |
+| `level_dimensions`| A sequence of down-sample factors for each level of the slide          |

_sources/cellxgene_census_docsite_landing.md.txt

@@ -1,4 +1,4 @@
-<span style="background-color: #f3bfcb; color; font-size: 18px"> 🚀 Now in testing: Spatial! From Jan 16th, latest builds will include data from Slide-seq and Visium assays. ⚠️ Opening these builds requires `tiledbsoma>=1.15.3`. ⚠️
+<span style="background-color: #f3bfcb; color; font-size: 18px"> 🚀 Now in testing: Spatial! From Jan 16th, latest builds will include data from Slide-seq and Visium assays. ⚠️ Opening these builds requires `tiledbsoma>=1.15.3` ⚠️. [Learn more](articles/2025/20250117-spatial.md)!
 
 <span style="background-color: #f3bfcb; color; font-size: 18px"> 🚀 New to the Census: Train PyTorch models directly with Census data with our efficient and easy-to-use PyTorch loaders. [Learn more](https://chanzuckerberg.github.io/cellxgene-census/articles/2024/20240709-pytorch.html)!
 

_sources/cellxgene_census_docsite_schema.md.txt

@@ -12,56 +12,65 @@ This page provides a user-friendly overview of the Census contents and its schem
 
 The Census is a collection of a variety of **[SOMA objects](#soma-objects)** organized with the following hierarchy.
 
-![image](cellxgene_census_docsite_model.svg)
+![image](census-spatial-schema.svg)
 
 As you can see the Census data is a `SOMACollection` with two high-level items:
 
 1. `"census_info"` for the census summary info.
 2. `"census_data"` for the single-cell data and metadata.
 
-### Census summary info `"census_info"`
+### Census Summary Info `"census_info"`
 
 A `SOMAcollection` with tables providing information of the census as a whole, it has the following items:
 
-- `"summary"`: high-level information of this Census, e.g. build date, total cell count, etc.
+- `"summary"`: High-level information of this Census, e.g., build date, total cell count, etc.
 - `"datasets"`: A table with all datasets from CELLxGENE Discover used to create the Census.
 - `"summary_cell_counts"`: Cell counts stratified by relevant cell metadata.
 
-### Census single-cell data `"census_data"`
+---
 
-Data for each organism is stored in independent `SOMAExperiment` objects which are a specialized form of a `SOMACollection`. Each of these store a data matrix (cell by genes), cell metadata, gene metadata, and feature presence matrix:
+### Census Single-Cell Data `"census_data"`
+
+Data for each organism is stored in independent `SOMAExperiment` objects, which are a specialized form of a `SOMACollection`. Each of these stores a data matrix (cell by genes), cell metadata, gene metadata, and feature presence matrix.
 
 This is how the data is organized for one organism – *Homo sapiens*:
 
 - `["homo_sapiens"].obs`: Cell metadata.
-- `["homo_sapiens"].ms["RNA"].X`: Data matrices: raw counts in `X["raw"]`, and library-size normalized counts in `X["normalized"]` (only avialble in Census schema V1.1.0 and above).
-- `["homo_sapiens"].ms["RNA"].var`: Gene Metadata.
-- `["homo_sapiens"].ms["RNA"]["feature_dataset_presence_matrix"]`: a sparse boolean array indicating which genes were measured in each dataset.
+- `["homo_sapiens"].ms["RNA"].X`: Data matrices: raw counts in `X["raw"]`, and library-size normalized counts in `X["normalized"]` (only available in Census schema V1.1.0 and above).
+- `["homo_sapiens"].ms["RNA"].var`: Gene metadata.
+- `["homo_sapiens"].ms["RNA"]["feature_dataset_presence_matrix"]`: A sparse boolean array indicating which genes were measured in each dataset.
+
+---
 
-## Data included in the Census
+### Data Included in the Census
 
 All data from [CZ CELLxGENE Discover](https://cellxgene.cziscience.com/) that adheres to the following criteria is included in the Census:
 
 - Cells from human or mouse.
-- Non-spatial RNA data, see full list of sequencing technologies included [here](https://github.com/chanzuckerberg/cellxgene-census/blob/main/docs/cellxgene_census_schema.md#assays).
+- **Spatial and non-spatial RNA data**, see the full list of sequencing technologies included [here](https://github.com/chanzuckerberg/cellxgene-census/blob/main/docs/cellxgene_census_schema.md#assays).
 - Raw counts.
 - Only standardized cell and gene metadata as described in the CELLxGENE Discover dataset [schema](https://github.com/chanzuckerberg/single-cell-curation/blob/main/schema/3.0.0/schema.md).
 
 ⚠️ Note that the data includes:
 
-- **Full-gene sequencing read counts** (e.g. Smart-Seq2) and **molecule counts** (e.g. 10X).
-- **Duplicate cells** present across multiple datasets, these can be filtered in or out using the cell metadata variable `is_primary_data`.
+- **Full-gene sequencing read counts** (e.g., Smart-Seq2) and **molecule counts** (e.g., 10X).
+- **Duplicate cells** present across multiple datasets. These can be filtered in or out using the cell metadata variable `is_primary_data`.
 
-## SOMA objects
+---
 
-You can find the full SOMA specification [here](https://github.com/single-cell-data/SOMA/blob/main/abstract_specification.md#foundational-types).
+### SOMA Objects
 
-The following is short description of the main SOMA objects used by the Census:
+You can find the full SOMA specification [here](https://github.com/single-cell-data/SOMA/blob/main/abstract_specification.md#foundational-types).
 
-- `DenseNDArray` is a dense, N-dimensional array, with offset (zero-based) integer indexing on each dimension.
-- `SparseNDArray` is the same as `DenseNDArray` but sparse, and supports point indexing (disjoint index access).
-- `DataFrame` is a multi-column table with a user-defined columns names and value types, with support for point indexing.
-- `Collection` is a persistent container of named SOMA objects.
-- `Experiment` is a class that represents a single-cell experiment. It always contains two objects:
-  - `obs`: a  `DataFrame` with primary annotations on the observation axis.
-  - `ms`: a  `Collection` of measurements, each composed of `X` matrices and axis annotation matrices or data frames (e.g. `var`, `varm`, `obsm`, etc).
+The following is a short description of the main SOMA objects used by the Census:
+
+- **`DenseNDArray`**: A dense, N-dimensional array, with offset (zero-based) integer indexing on each dimension.
+- **`SparseNDArray`**: The same as `DenseNDArray` but sparse, and supports point indexing (disjoint index access).
+- **`DataFrame`**: A multi-column table with user-defined column names and value types, with support for point indexing.
+- **`Collection`**: A persistent container of named SOMA objects.
+- **`Experiment`**: A class that represents a single-cell experiment. It always contains two objects:
+  - `obs`: A `DataFrame` with primary annotations on the observation axis.
+  - `ms`: A `Collection` of measurements, each composed of `X` matrices and axis annotation matrices or data frames (e.g., `var`, `varm`, `obsm`, etc.).
+- **`SOMAScene`**: A `Collection` of `obsl`, `varl`, and `img`.
+- **`Spatial`**: A collection of `Scene` objects.
+- **`obs_spatial_presence`**: A `DataFrame` to map observations to `Scene` objects.

articles.html

@@ -160,6 +160,10 @@
 <li class="toctree-l3"><a class="reference internal" href="articles/2024/20240710_embedding_metrics_dec_2023_lts.html">Benchmarks of single-cell Census models</a></li>
 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="#id3">2025</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="articles/2025/20250117-spatial.html">Beta Release of Spatial Data on Census!</a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="cellxgene_census_aws_open_data.html">Census in AWS ☁️</a></li>
@@ -229,6 +233,14 @@ <h2>2024<a class="headerlink" href="#id2" title="Permalink to this heading"><
 </ul>
 </div>
 </section>
+<section id="id3">
+<h2>2025<a class="headerlink" href="#id3" title="Permalink to this heading"></a></h2>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="articles/2025/20250117-spatial.html">Beta Release of Spatial Data on Census!</a></li>
+</ul>
+</div>
+</section>
 </section>
 
 

articles/2023/20230808-r_api_release.html

@@ -160,6 +160,7 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../articles.html#id2">2024</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../articles.html#id3">2025</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../cellxgene_census_aws_open_data.html">Census in AWS ☁️</a></li>

articles/2023/20230919-out_of_core_methods.html

@@ -159,6 +159,7 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../articles.html#id2">2024</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../articles.html#id3">2025</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../cellxgene_census_aws_open_data.html">Census in AWS ☁️</a></li>

articles/2023/20231012-normalized_layer_precalc_stats.html

@@ -160,6 +160,7 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../articles.html#id2">2024</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../articles.html#id3">2025</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../cellxgene_census_aws_open_data.html">Census in AWS ☁️</a></li>

articles/2024/20240404-categoricals.html

@@ -159,6 +159,7 @@
 <li class="toctree-l3"><a class="reference internal" href="20240710_embedding_metrics_dec_2023_lts.html">Benchmarks of single-cell Census models</a></li>
 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="../../articles.html#id3">2025</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../cellxgene_census_aws_open_data.html">Census in AWS ☁️</a></li>

articles/2024/20240709-pytorch.html

@@ -159,6 +159,7 @@
 <li class="toctree-l3"><a class="reference internal" href="20240710_embedding_metrics_dec_2023_lts.html">Benchmarks of single-cell Census models</a></li>
 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="../../articles.html#id3">2025</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../cellxgene_census_aws_open_data.html">Census in AWS ☁️</a></li>

articles/2024/20240710_embedding_metrics_dec_2023_lts.html

@@ -48,7 +48,7 @@
 
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="CZ CELLxGENE Discover Census in AWS" href="../../cellxgene_census_aws_open_data.html" />
+    <link rel="next" title="Beta Release of Spatial Data on Census!" href="../2025/20250117-spatial.html" />
     <link rel="prev" title="First stable iteration of Census (SOMA) PyTorch loaders" href="20240709-pytorch.html" /> 
 </head>
 
@@ -160,6 +160,7 @@
 </li>
 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="../../articles.html#id3">2025</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../cellxgene_census_aws_open_data.html">Census in AWS ☁️</a></li>
@@ -525,7 +526,7 @@ <h4>Spinal cord<a class="headerlink" href="#spinal-cord" title="Permalink to thi
             </div>
             <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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