# Cellxgene Census

> Consulta programáticamente la CZ CELLxGENE Census: metadatos y expresión de single-cell/espacial versionados a escala poblacional, entre organismos, tejidos, enfermedades y ensayos, sin descargar datasets enteros.

Fuente: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/cellxgene-census
Markdown: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/cellxgene-census.md

Repositorio: https://github.com/K-Dense-AI/scientific-agent-skills
Autor: K-Dense-AI
Licencia: MIT
Actualizado: el mes pasado
Coste de contexto: 111 tok instalada, 2.6k tok al activarse, 9.8k tok con todos los archivos del bundle
Bundle: 4 archivos, 38 KB
Permisos que pide: read write edit bash

## Instalación

Un skill son archivos markdown: los mismos archivos valen para cualquier agente y lo único que cambia es el directorio de destino, es decir la bandera `--agent`. Añade `-g` para instalarlo en todos los proyectos de la máquina.

```bash
# Claude Code
npx -y skills add K-Dense-AI/scientific-agent-skills --skill cellxgene-census --agent claude-code
# Cursor
npx -y skills add K-Dense-AI/scientific-agent-skills --skill cellxgene-census --agent cursor
# Codex
npx -y skills add K-Dense-AI/scientific-agent-skills --skill cellxgene-census --agent codex
# Gemini CLI
npx -y skills add K-Dense-AI/scientific-agent-skills --skill cellxgene-census --agent gemini
# Windsurf
npx -y skills add K-Dense-AI/scientific-agent-skills --skill cellxgene-census --agent windsurf
# Cline
npx -y skills add K-Dense-AI/scientific-agent-skills --skill cellxgene-census --agent cline
```

## Qué hace

- Consulta la CZ CELLxGENE Census de forma programática para metadatos y expresión de single-cell y espacial a gran escala
- Permite explorar metadatos antes de descargar (tipo celular, tejido, enfermedad, ensayo) y luego consultar expresión con AnnData
- Cubre consultas grandes fuera de memoria, carga de datos para PyTorch y acceso a datos espaciales del Census
- Facilita comparaciones entre organismos, tejidos y datasets usando embeddings y matrices de presencia precalculadas

## Cuándo usarla

- Se necesita consultar expresión de single-cell por tipo celular, tejido o enfermedad sin descargar datasets completos
- Se quiere explorar datasets y metadatos disponibles en el Census antes de analizar
- Se van a entrenar modelos de ML sobre datos de single-cell a gran escala
- Se necesita integrar datos del Census con scanpy u otros frameworks de análisis

## Cuándo no

- Los datos son propios/locales de single-cell, no del Census público (usar scanpy, anndata o scvi-tools)

## Qué la activa

- "Consulta el Census de CELLxGENE para células T en pulmón humano"
- "Trae expresión de CD4 y CD8A en COVID-19 desde el Census"
- "Explora qué datasets del Census cubren tejido cerebral"

## Antes de instalar

- Requiere `uv pip install cellxgene-census`; no hace falta autenticación para los datos públicos del Census, y los workflows espaciales piden el extra `[spatial]` y TileDB-SOMA >=1.15.5.

## Archivos

- SKILL.md — 10 KB
- references/census_schema.md — 8 KB
- references/common_patterns.md — 11 KB
- references/core_workflow_patterns.md — 10 KB

## SKILL.md

Reproducido tal cual desde K-Dense-AI/scientific-agent-skills bajo MIT. Esta sección es el documento original y está en inglés.

# CZ CELLxGENE Census

## Overview

The CZ CELLxGENE Census provides programmatic access to a comprehensive, versioned collection of standardized single-cell and spatial transcriptomics data from CZ CELLxGENE Discover. This skill enables efficient querying and analysis of public Census releases without downloading whole datasets first.

The Census includes:
- **217+ million total cells** and **125+ million unique cells** in the 2025-11-08 stable LTS release
- **1,845 datasets** in the 2025-11-08 stable LTS release
- **Human, mouse, marmoset, rhesus macaque, and chimpanzee** data in the current schema
- **Standardized metadata** (cell types, tissues, diseases, donors)
- **Raw gene expression** matrices and source H5AD lookup/download helpers
- **Pre-calculated summary counts, embeddings, and spatial data**
- **Integration with AnnData, Scanpy, TileDB-SOMA, TileDB-SOMA-ML, and other analysis tools**

## When to Use This Skill

This skill should be used when:
- Querying single-cell expression data by cell type, tissue, or disease
- Exploring available single-cell datasets and metadata
- Training machine learning models on single-cell data
- Performing large-scale cross-dataset analyses
- Integrating Census data with scanpy or other analysis frameworks
- Computing statistics across millions of cells
- Accessing pre-calculated embeddings or model predictions

## Installation and Setup

Install the Census API:
```bash
uv pip install "cellxgene-census==1.17.*"
```

For spatial workflows:
```bash
uv pip install "cellxgene-census[spatial]==1.17.*" "spatialdata[extra]>=0.2.5"
```

For PyTorch model training, use TileDB-SOMA-ML. The old `cellxgene_census.experimental.ml` loaders are deprecated:

```bash
uv pip install "cellxgene-census==1.17.*" tiledbsoma-ml
```

## Core Workflow Patterns

Eight patterns, each with code, are in
[references/core_workflow_patterns.md](references/core_workflow_patterns.md):

1. **Opening the Census** — always pin `census_version` so an analysis stays reproducible.
2. **Exploring Census information** — available datasets, cell counts, and summary tables.
3. **Querying expression data** — small to medium scale into an `AnnData`.
4. **Large-scale queries** — out-of-core processing when the slice will not fit in memory.
5. **Machine learning with PyTorch** — the Census data loaders.
6. **Spatial Census data** — accessing spatial assays.
7. **Integration with Scanpy** — handing a Census slice to a standard Scanpy workflow.
8. **Multi-dataset integration** — combining datasets and handling batch effects.

## Key Concepts and Best Practices

### Always Filter for Primary Data
Unless analyzing duplicates, always include `is_primary_data == True` in queries to avoid counting cells multiple times:
```python
obs_value_filter="cell_type == 'B cell' and is_primary_data == True"
```

### Specify Census Version for Reproducibility
Always specify the Census version in production analyses:
```python
census = cellxgene_census.open_soma(census_version="2025-11-08")
```

### Estimate Query Size Before Loading
For large queries, first check the number of cells to avoid memory issues:
```python
# Get cell count
metadata = cellxgene_census.get_obs(
    census, "homo_sapiens",
    value_filter="tissue_general == 'brain' and is_primary_data == True",
    column_names=["soma_joinid"]
)
n_cells = len(metadata)
print(f"Query will return {n_cells:,} cells")

# If too large (>100k), use out-of-core processing
```

### Use tissue_general for Broader Groupings
The `tissue_general` field provides coarser categories than `tissue`, useful for cross-tissue analyses:
```python
# Broader grouping
obs_value_filter="tissue_general == 'immune system'"

# Specific tissue
obs_value_filter="tissue == 'peripheral blood mononuclear cell'"
```

### Select Only Needed Columns
Minimize data transfer by specifying only required metadata columns:
```python
obs_column_names=["cell_type", "tissue_general", "disease"]  # Not all columns
```

### Check Dataset Presence for Gene-Specific Queries
When analyzing specific genes, verify which datasets measured them:
```python
presence = cellxgene_census.get_presence_matrix(
    census,
    "homo_sapiens",
    var_value_filter="feature_name in ['CD4', 'CD8A']"
)
```

### Two-Step Workflow: Explore Then Query
First explore metadata to understand available data, then query expression:
```python
# Step 1: Explore what's available
metadata = cellxgene_census.get_obs(
    census, "homo_sapiens",
    value_filter="disease == 'COVID-19' and is_primary_data == True",
    column_names=["cell_type", "tissue_general"]
)
print(metadata.value_counts())

# Step 2: Query based on findings
adata = cellxgene_census.get_anndata(
    census=census,
    organism="Homo sapiens",
    obs_value_filter="disease == 'COVID-19' and cell_type == 'T cell' and is_primary_data == True",
)
```

## Available Metadata Fields

### Cell Metadata (obs)
Key fields for filtering:
- `cell_type`, `cell_type_ontology_term_id`
- `tissue`, `tissue_general`, `tissue_ontology_term_id`
- `disease`, `disease_ontology_term_id`
- `assay`, `assay_ontology_term_id`
- `donor_id`, `sex`, `self_reported_ethnicity`
- `development_stage`, `development_stage_ontology_term_id`
- `dataset_id`
- `is_primary_data` (Boolean: True = unique cell)

The current schema includes organism collections beyond human and mouse. Confirm available organisms for the selected release with `list(census["census_data"].keys())`.

### Gene Metadata (var)
- `feature_id` (Ensembl gene ID, e.g., "ENSG00000161798")
- `feature_name` (Gene symbol, e.g., "FOXP2")
- `feature_type`
- `feature_length` (Gene length in base pairs)
- `nnz`, `n_measured_obs` (availability summaries useful for checking sparsity and coverage)

## Reference Documentation

This skill includes detailed reference documentation:

### references/census_schema.md
Comprehensive documentation of:
- Census data structure and organization
- All available metadata fields
- Value filter syntax and operators
- SOMA object types
- Data inclusion criteria

**When to read:** When you need detailed schema information, full list of metadata fields, or complex filter syntax.

### references/common_patterns.md
Examples and patterns for:
- Exploratory queries (metadata only)
- Small-to-medium queries (AnnData)
- Large queries (out-of-core processing)
- PyTorch integration
- Spatial Census access patterns
- Scanpy integration workflows
- Multi-dataset integration
- Best practices and common pitfalls

**When to read:** When implementing specific query patterns, looking for code examples, or troubleshooting common issues.

## Common Use Cases

### Use Case 1: Explore Cell Types in a Tissue
```python
with cellxgene_census.open_soma() as census:
    cells = cellxgene_census.get_obs(
        census, "homo_sapiens",
        value_filter="tissue_general == 'lung' and is_primary_data == True",
        column_names=["cell_type"]
    )
    print(cells["cell_type"].value_counts())
```

### Use Case 2: Query Marker Gene Expression
```python
with cellxgene_census.open_soma() as census:
    adata = cellxgene_census.get_anndata(
        census=census,
        organism="Homo sapiens",
        var_value_filter="feature_name in ['CD4', 'CD8A', 'CD19']",
        obs_value_filter="cell_type in ['T cell', 'B cell'] and is_primary_data == True",
    )
```

### Use Case 3: Train Cell Type Classifier
```python
import tiledbsoma as soma
from tiledbsoma_ml import ExperimentDataset, experiment_dataloader

with cellxgene_census.open_soma() as census:
    experiment = census["census_data"]["homo_sapiens"]
    with experiment.axis_query(
        measurement_name="RNA",
        obs_query=soma.AxisQuery(value_filter="is_primary_data == True"),
    ) as query:
        dataset = ExperimentDataset(
            query=query,
            layer_name="raw",
            obs_column_names=["cell_type"],
            batch_size=128,
            shuffle=True,
        )
        dataloader = experiment_dataloader(dataset)

        for X, obs in dataloader:
            labels = obs["cell_type"]
            # Training logic
            pass
```

### Use Case 4: Cross-Tissue Analysis
```python
with cellxgene_census.open_soma() as census:
    adata = cellxgene_census.get_anndata(
        census=census,
        organism="Homo sapiens",
        obs_value_filter="cell_type == 'macrophage' and tissue_general in ['lung', 'liver', 'brain'] and is_primary_data == True",
    )

    # Analyze macrophage differences across tissues
    sc.tl.rank_genes_groups(adata, groupby="tissue_general")
```

## Troubleshooting

### Query Returns Too Many Cells
- Add more specific filters to reduce scope
- Use `tissue` instead of `tissue_general` for finer granularity
- Filter by specific `dataset_id` if known
- Switch to out-of-core processing for large queries

### Memory Errors
- Reduce query scope with more restrictive filters
- Select fewer genes with `var_value_filter`
- Use out-of-core processing with `axis_query()`
- Process data in batches

### Duplicate Cells in Results
- Always include `is_primary_data == True` in filters
- Check if intentionally querying across multiple datasets

### Gene Not Found
- Verify gene name spelling (case-sensitive)
- Try Ensembl ID with `feature_id` instead of `feature_name`
- Check dataset presence matrix to see if gene was measured
- Some genes may have been filtered during Census construction

### Version Inconsistencies
- Always specify `census_version` explicitly
- Use same version across all analyses
- Check release notes for version-specific changes

## Dónde encaja

- Categoría: [Datos y analítica](https://skillsagentes.com/categorias/datos-analitica.md) — Consulta, limpia y visualiza datos sin salir del agente.
- Creador: [K-Dense-AI](https://skillsagentes.com/creators/k-dense-ai.md) — 163 skills en el directorio
- [Todas las skills](https://skillsagentes.com/skills.md)
- [Ranking de instalaciones](https://skillsagentes.com/ranking.md)

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- [Literature Review](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/literature-review.md): Realiza revisiones bibliográficas sistemáticas y completas usando varias bases académicas (PubMed, arXiv, bioRxiv, Semantic Scholar). Genera markdown y PDF con citas verificadas en varios estilos (APA, Nature, Vancouver).
- [Infographics](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/infographics.md): Crea infografías profesionales con Nano Banana Pro AI y refinamiento iterativo inteligente. Usa Gemini 3.6 Flash para revisar la calidad e integra investigación con Perplexity Sonar. Soporta 10 tipos, 8 estilos y paletas para daltonismo.
- [Latex Posters](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/latex-posters.md): Crea pósteres de investigación profesionales en LaTeX con beamerposter, tikzposter o baposter, para conferencias y comunicación científica: layout, colores, columnas múltiples e integración de figuras.

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