gh-k-dense-ai-claude-scientific-skills-scientific-skills/skills/cellxgene-census/references/census_schema.md

CZ CELLxGENE Census Data Schema Reference

Overview

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

High-Level Structure

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

1. census_info

Summary information including:

• summary: Build date, cell counts, dataset statistics
• datasets: All datasets from CELLxGENE Discover with metadata
• 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

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: (if available) Normalized counts
• var: Gene metadata
• feature_dataset_presence_matrix: Sparse boolean array showing which genes were measured in each dataset

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 = unique cell, 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

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: Unique donor identifier
• 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:

• organism: Scientific name (Homo sapiens, Mus musculus)
• organism_ontology_term_id: Standardized ontology term

Technical:

• suspension_type: Sample preparation type (cell, nucleus, na)

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")
• feature_length: Gene length in base pairs

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']"

Data Inclusion Criteria

The Census includes all data from CZ CELLxGENE Discover meeting:

1. Species: Human (Homo sapiens) or mouse (Mus musculus)
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. Both spatial and non-spatial data: Includes traditional and spatial transcriptomics

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., "2023-07-25", "stable"). Always specify version for reproducible analysis:

census = cellxgene_census.open_soma(census_version="2023-07-25")

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: Spatial data availability