CZ CELLxGENE Census Data Schema Reference

Overview

The CZ CELLxGENE Census is a versioned collection of single-cell and spatial transcriptomics data built on the TileDB-SOMA framework. This reference documents the data structure, available metadata fields, and query syntax.

Current reference point:

• Package examples target cellxgene-census==1.18.0
• Current stable LTS Census: 2025-11-08
• Census schema version: 2.4.0
• CELLxGENE dataset schema version: 7.0.0
• SDK tested against this LTS: cellxgene-census 1.18.0 / TileDB-SOMA 2.3.0
• Sources: official schema and release notes, reviewed 2026-09-30.

High-Level Structure

The Census is organized as a SOMACollection with these main components:

1. census_info

Summary information including:

• summary: Build date, cell counts, dataset statistics
• datasets: Source datasets included in this release (not all of Discover); fields include dataset_id, dataset_version_id, dataset_title, collection_id, collection_name, collection_doi, citation, dataset_h5ad_path, and dataset_total_cell_count. Disease and tissue live on obs, not this table.
• organisms: organism, organism_label, and organism_ontology_term_id mappings
• summary_cell_counts: Cell counts stratified by metadata categories

2. census_data

Organism-specific SOMAExperiment objects:

• "homo_sapiens": Human single-cell data
• "mus_musculus": Mouse single-cell data
• "callithrix_jacchus": Common marmoset single-cell data
• "macaca_mulatta": Rhesus macaque single-cell data
• "pan_troglodytes": Chimpanzee single-cell data

3. census_spatial_sequencing

Spatial organism-specific SOMAExperiment objects for supported releases. Spatial and non-spatial data share core metadata requirements, while spatial observations also include spatial columns such as array_col, array_row, and in_tissue.

Single-Cell Data Structure Per Organism

Each organism experiment contains:

obs (Cell Metadata)

Cell-level annotations stored as a SOMADataFrame. Access via:

census["census_data"]["homo_sapiens"].obs

ms["RNA"] (Measurement)

RNA measurement data including:

• X: Data matrices with layers:
• raw: Raw count data
• normalized: Library-size-normalized values; full-gene assays also receive gene-length normalization. This is not log-transformed. Inspect list(experiment.ms["RNA"].X) for the selected release. Spatial data provides raw only.
• var: Gene metadata
• feature_dataset_presence_matrix: Sparse boolean array showing which genes were measured in each dataset

Spatial Data Structure Per Organism

Spatial data is stored separately from the single-cell Census data:

census["census_spatial_sequencing"]["homo_sapiens"]

Each spatial organism experiment contains:

• obs: Spatial observation metadata, including core Census metadata and spatial fields such as array_col, array_row, and in_tissue
• ms["RNA"]: RNA measurement matrices and feature metadata
• spatial[scene_id].obsl["loc"]: point-cloud positions for each scene, with x, y, and soma_joinid

Use axis_query(...).to_spatialdata(X_name="raw") when exporting a spatial slice to spatialdata.

Cell Metadata Fields (obs)

Required/Core Fields

Identity & Dataset:

• soma_joinid: Unique integer identifier for joins
• dataset_id: Source dataset identifier
• is_primary_data: Boolean flag (True = primary representation, False = duplicate across datasets)

Cell Type:

• cell_type: Human-readable cell type name
• cell_type_ontology_term_id: Standardized ontology term (e.g., "CL:0000236")

Tissue:

• tissue: Specific tissue name
• tissue_general: Broader tissue category (useful for grouping)
• tissue_ontology_term_id: Standardized ontology term
• tissue_general_ontology_term_id: Standardized ontology term for the broader tissue category

Assay:

• assay: Sequencing technology used
• assay_ontology_term_id: Standardized ontology term

Disease:

• disease: Disease status or condition
• disease_ontology_term_id: Standardized ontology term

Donor:

• donor_id: Source-provided donor identifier; do not assume global uniqueness across studies
• sex: Biological sex (male, female, unknown)
• self_reported_ethnicity: Ethnicity information
• development_stage: Life stage (adult, child, embryonic, etc.)
• development_stage_ontology_term_id: Standardized ontology term

Organism: Select the experiment by its organism key. Organism labels/ontology IDs are in census_info["organisms"], not guaranteed obs columns.

Technical:

• suspension_type: Sample preparation type (cell, nucleus, na)
• tissue_type: tissue or organoid for included observations
• observation_joinid: Source observation identity
• nnz, n_measured_vars, raw_sum, raw_mean_nnz, raw_variance_nnz: Count summaries; the _nnz statistics exclude zeros

Gene Metadata Fields (var)

Access via:

census["census_data"]["homo_sapiens"].ms["RNA"].var

Available Fields:

• soma_joinid: Unique integer identifier for joins
• feature_id: Ensembl gene ID (e.g., "ENSG00000161798")
• feature_name: Gene symbol (e.g., "FOXP2"); duplicates are allowed since schema 2.4.0, so use feature_id as the stable feature key
• feature_type: Present in the verified 2025-11-08 build, although omitted from the upstream schema field list; inspect the live schema
• feature_length: Gene length in base pairs
• nnz: Non-zero count summary
• n_measured_obs: Number of measured observations for the feature

Value Filter Syntax

Queries use Python-like expressions for filtering. The syntax is processed by TileDB-SOMA.

Comparison Operators

• ==: Equal to
• !=: Not equal to
• <, >, <=, >=: Numeric comparisons
• in: Membership test (e.g., feature_id in ['ENSG00000161798', 'ENSG00000188229'])

Logical Operators

• and, &: Logical AND
• or, |: Logical OR

Examples

Single condition:

value_filter="cell_type == 'B cell'"

Multiple conditions with AND:

value_filter="cell_type == 'B cell' and tissue_general == 'lung' and is_primary_data == True"

Using IN for multiple values:

value_filter="tissue in ['lung', 'liver', 'kidney']"

Complex condition:

value_filter="(cell_type == 'neuron' or cell_type == 'astrocyte') and disease != 'normal'"

Filtering genes:

var_value_filter="feature_name in ['CD4', 'CD8A', 'CD19']"

Multi-Value Disease Fields

In current LTS releases, disease and disease_ontology_term_id may contain multiple values delimited by || . Exact equality filters such as disease == 'COVID-19' can miss cells whose disease field contains multiple labels. For comprehensive disease queries, first inspect available values with get_obs() or summary_cell_counts, then choose filters that match the selected release's encoding.

Data Inclusion Criteria

The Census includes all data from CZ CELLxGENE Discover meeting:

1. Species: Supported human, mouse, marmoset, rhesus macaque, and chimpanzee data; inspect the selected release's organism table and collections
2. Technology: Approved sequencing technologies for RNA
3. Count Type: Raw counts only (no processed/normalized-only data)
4. Metadata: Standardized following CELLxGENE schema
5. Sample type: Tissue and organoid observations; primary cell culture is excluded
6. Both spatial and non-spatial data: Includes qualifying assays, with spatial data stored separately

Important Data Characteristics

Duplicate Cells

Cells may appear across multiple datasets. Use is_primary_data == True to filter for unique cells in most analyses.

Count Types

The Census includes:

• Molecule counts: From UMI-based methods
• Full-gene sequencing read counts: From non-UMI methods

These may need different normalization approaches.

Versioning

Census releases are versioned (e.g., "2025-11-08", "stable", "latest"). Always specify an LTS build date for reproducible analysis:

census = cellxgene_census.open_soma(census_version="2025-11-08")

stable resolves to the current LTS release. latest resolves to the newest weekly release, which provides fast access to newly ingested datasets but is retained for a shorter period than LTS releases.

Feature Dataset Presence Matrix

Access which genes were measured in each dataset:

presence_matrix = census["census_data"]["homo_sapiens"].ms["RNA"]["feature_dataset_presence_matrix"]

This sparse boolean matrix helps understand:

• Gene coverage across datasets
• Which datasets to include for specific gene analyses
• Technical batch effects related to gene coverage

SOMA Object Types

Core TileDB-SOMA objects used:

• DataFrame: Tabular data (obs, var)
• SparseNDArray: Sparse matrices (X layers, presence matrix)
• DenseNDArray: Dense arrays (less common)
• Collection: Container for related objects
• Experiment: Top-level container for measurements
• SOMAScene: Spatial transcriptomics scenes
• obs_spatial_presence: A DataFrame mapping observations to scenes, not a separate SOMA object type