/cellxgene-context

name: cellxgene-context
description: "Guide for integrating CellxGene Census single-cell data with ENCODE bulk experiments. Use when users need cell-type-specific expression context for ENCODE regulatory data, want to deconvolve bulk ENCODE signals, or validate regulatory elements at single-cell resolution. Trigger on: CellxGene, single-cell atlas, cell type expression, Census, cell type specificity, single-cell context, scRNA-seq atlas."

Integrating CellxGene Census Single-Cell Data with ENCODE Bulk Experiments

Bridge bulk ENCODE functional genomics data with cell-type-specific expression from the CellxGene Census, the largest unified single-cell RNA-seq atlas, to resolve cell-type contributions to regulatory element activity.

Scientific Rationale

**The question**: "Which specific cell types within my tissue drive the regulatory signals I see in bulk ENCODE data?"

ENCODE provides deeply sequenced bulk functional genomics (ChIP-seq, ATAC-seq, Hi-C) across hundreds of biosamples. But bulk data from a tissue like "pancreas" is a mixture of acinar cells (~80%), duct cells (~10%), endocrine cells (~5%), and others. An H3K27ac peak in bulk pancreas could be driven by any of these cell types. CellxGene Census provides cell-type-resolved expression data from 50M+ single-cell observations across thousands of datasets, enabling deconvolution of bulk ENCODE signals.

The Bulk-to-Single-Cell Bridge

| Bulk ENCODE Signal | Single-Cell Question | CellxGene Answer | |-------------------|---------------------|-----------------| | H3K27ac peak near INS gene in pancreas | Which cell type expresses INS? | Beta cells (>500 TPM), not acinar (<1 TPM) | | ATAC-seq peak in liver near ALB | Is this hepatocyte-specific? | Yes — ALB expressed only in hepatocytes | | Enhancer active in brain cortex | Neurons or glia? | CellxGene resolves excitatory neurons vs. astrocytes vs. oligodendrocytes | | Broad H3K27ac domain in blood | Which immune cell type? | Can distinguish T cells, B cells, monocytes, NK cells |

What CellxGene Census Provides

• **50M+ single-cell observations** from thousands of published datasets
• **Standardized cell ontology** (Cell Ontology terms) across all datasets
• **Unified gene expression** in a consistent format
• **Metadata**: tissue, disease status, sex, ethnicity, developmental stage
• **API access** via Python (`cellxgene-census`) or R (`cellxgene.census`)
• **No authentication required** for public data

Key Literature

• **Megill et al. 2021** "cellxgene: a performant, scalable exploration platform for high dimensional sparse matrices" (bioRxiv preprint). Describes the CellxGene platform architecture and exploration capabilities. [DOI: 10.1101/2021.04.05.438318](https://doi.org/10.1101/2021.04.05.438318)
• **CZ CELLxGENE Discover** (Chan Zuckerberg Initiative, 2023). CellxGene Census provides programmatic access to the entire CellxGene data corpus as a single unified dataset. [https://cellxgene.cziscience.com/](https://cellxgene.cziscience.com/)
• **Regev et al. 2017** "The Human Cell Atlas" (eLife, ~1,500 citations). The vision paper for comprehensive single-cell reference maps of all human cells. CellxGene Census is the largest realization of this vision. [DOI: 10.7554/eLife.27041](https://doi.org/10.7554/eLife.27041)
• **Tabula Sapiens Consortium 2022** "The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans" (Science, ~800 citations). Multi-organ human cell atlas contributing to CellxGene Census. [DOI: 10.1126/science.abl4896](https://doi.org/10.1126/science.abl4896)
• **ENCODE Project Consortium 2020** (Nature, ~1,656 citations). The bulk regulatory element catalog that CellxGene single-cell data contextualizes. [DOI: 10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)

When to Use This Skill

| Scenario | How CellxGene Helps | |---------|-------------------| | Bulk ENCODE peak near a gene — which cell type? | Query gene expression by cell type in matching tissue | | ENCODE enhancer active in tissue X — cell-type-specific? | Check if enhancer target gene is restricted to one cell type | | Choosing ENCODE cell line as proxy | Verify which primary cell type the cell line best represents | | Interpreting differential peaks between tissues | Determine if difference is due to cell-type composition | | Validating ENCODE scATAC-seq findings | Cross-reference with CellxGene scRNA-seq for same cell types | | Designing follow-up experiments | Identify which cell types to isolate for validation |

Python API Reference

Installation

pip install cellxgene-census

Requires Python 3.8+. The package uses TileDB-SOMA for efficient data access.

Core API Pattern

import cellxgene_census

# Open the Census (reads metadata, does not download all data)
with cellxgene_census.open_soma() as census:
    # Access human data
    human = census["census_data"]["homo_sapiens"]

    # Query specific genes in specific tissues/cell types
    # This is where filtering happens — be specific to control memory

Step 1: Identify the ENCODE Target Gene

Start from an ENCODE finding — a regulatory element near a gene of interest:

# Find enhancers in pancreas
encode_search_experiments(
    assay_title="Histone ChIP-seq",
    target="H3K27ac",
    organ="pancreas",
    biosample_type="tissue"
)

# Get peaks
encode_list_files(
    experiment_accession="ENCSR...",
    file_format="bed",
    output_type="IDR thresholded peaks",
    assembly="GRCh38"
)

From peaks, identify the nearest gene(s). You need the gene symbol or Ensembl ID.

Step 2: Query CellxGene Census for Cell-Type Expression

Basic Gene Expression Query

import cellxgene_census
import pandas as pd

gene_symbol = "INS"  # Insulin — example for pancreas

with cellxgene_census.open_soma() as census:
    human = census["census_data"]["homo_sapiens"]

    # Get expression for INS in pancreas tissue
    # Use obs_value_filt