/single-cell-encode

name: single-cell-encode
description: Find and work with ENCODE single-cell genomics data including scRNA-seq and scATAC-seq. Use when the user asks about single-cell experiments, cell type resolution, clustering from ENCODE data, deconvolution of bulk signals using single-cell references, or comparing single-cell vs bulk profiles. Covers platform differences (10X Chromium, Smart-seq2, Drop-seq), quality limitations of single-cell data, multimodal integration (RNA+ATAC), and cross-study reproducibility concerns. Also use for cell type annotation, gene detection limits, dropout artifacts, and single-cell data structure in ENCODE.

Single-Cell ENCODE Data

When to Use

• User wants to find or analyze single-cell data (scRNA-seq, scATAC-seq, snRNA-seq) from ENCODE
• User asks about "single-cell", "scRNA-seq", "scATAC-seq", "cell type annotation", or "single-nucleus"
• User needs to integrate ENCODE single-cell data with bulk epigenomic profiles
• User wants to identify cell-type-specific regulatory elements from single-cell chromatin accessibility
• Example queries: "find scRNA-seq data in ENCODE for brain", "what snATAC-seq is available?", "integrate single-cell with bulk ChIP-seq"

Help the user find and work with ENCODE single-cell genomics data, understand quality limitations relative to bulk assays, and integrate single-cell with bulk ENCODE profiles for cell-type-resolved regulatory analysis.

Literature Foundation

| # | Reference | Key Contribution | |---|-----------|-----------------| | 1 | Mawla & Huising 2019, Endocrinology, DOI:10.1210/en.2018-01037 (~200 cit) | Cross-study scRNA-seq meta-analysis revealing that only ~1-2% of heterogeneity-driving genes replicate across studies; TIN-based quality assessment; detection-limit awareness framework. PMC6609986. | | 2 | Regev et al. 2017, eLife, DOI:10.7554/eLife.27041 (~1,200 cit) | Human Cell Atlas white paper defining the vision for comprehensive single-cell reference maps of all human cells. Establishes community standards for cell atlas construction. | | 3 | Stuart et al. 2019, Cell, DOI:10.1016/j.cell.2019.05.031 (~7,000 cit) | Seurat v3 — CCA-based anchor identification for cross-dataset integration. The most widely used scRNA-seq integration framework. | | 4 | Luecken & Theis 2019, Mol Syst Biol, DOI:10.15252/msb.20188746 (~1,500 cit) | Current best practices for scRNA-seq analysis: QC, normalization, batch correction, feature selection, dimensionality reduction, clustering, and differential expression. | | 5 | Buenrostro et al. 2015, Nature, DOI:10.1038/nature14590 (~1,800 cit) | Single-cell ATAC-seq method. Established that individual cells yield the same nucleosomal fragment size ladder as bulk ATAC-seq, enabling chromatin accessibility profiling at single-cell resolution. | | 6 | Granja et al. 2021, Nat Genet, DOI:10.1038/s41588-021-00790-6 (~1,000 cit) | ArchR — scalable framework for scATAC-seq analysis including peak calling, gene activity scoring, trajectory inference, and integration with scRNA-seq. | | 7 | Luecken et al. 2022, Nat Methods, DOI:10.1038/s41592-021-01336-8 (~800 cit) | Benchmarking atlas-level integration methods across tasks, metrics, and scalability. Establishes evaluation framework (kBET, LISI, ARI, NMI) for comparing integration quality. | | 8 | Hao et al. 2021, Cell, DOI:10.1016/j.cell.2021.04.048 (~5,000 cit) | Seurat v4 — weighted nearest neighbors (WNN) for multimodal integration of RNA + ATAC (or CITE-seq). Defines the standard for joint profiling analysis. | | 9 | ENCODE Project Consortium 2020, Nature, DOI:10.1038/s41586-020-2493-4 (~1,656 cit) | ENCODE Phase 3; registry of candidate cis-regulatory elements (cCREs) providing the bulk reference against which single-cell data can be compared. |

Available Single-Cell Assays in ENCODE

| Assay | What It Measures | Key Outputs | Typical Files in ENCODE | |-------|-----------------|-------------|------------------------| | scRNA-seq | Single-cell gene expression | Cell-type-specific transcriptomes | FASTQ, gene quantifications (TSV), filtered count matrices, h5ad | | scATAC-seq | Single-cell chromatin accessibility | Cell-type-specific regulatory elements | FASTQ, fragments (TSV), aggregate peaks (BED), cell-barcode assignments |

Step 1: Search for Single-Cell Data in ENCODE

Search for scRNA-seq and scATAC-seq experiments in the tissue of interest:

# Single-cell RNA-seq
encode_search_experiments(
    assay_title="scRNA-seq",
    organ="pancreas",           # user's tissue of interest
    biosample_type="tissue",
    limit=50
)

# Single-cell ATAC-seq
encode_search_experiments(
    assay_title="snATAC-seq",
    organ="pancreas",
    biosample_type="tissue",
    limit=50
)

If no results, try broader search terms:

encode_search_experiments(search_term="single cell RNA", organ="pancreas", limit=50)
encode_search_experiments(search_term="single cell ATAC", organ="pancreas", limit=50)

Check facets first to understand what organs have single-cell data:

encode_get_facets(assay_title="scRNA-seq")
encode_get_facets(assay_title="snATAC-seq")

Present a summary to the user showing:

• Number of scRNA-seq and scATAC-seq experiments found
• Organs/tissues represented
• Platforms used (10X Chromium, Smart-seq2, Drop-seq)
• Labs contributing data
• Number of unique donors/biosamples

Step 2: Understand ENCODE Single-Cell Data Structure

scRNA-seq Files

Use `encode_list_files` to see what is available per experiment:

encode_list_files(
    experiment_accession="ENCSR...",
    assembly="GRCh38",
    preferred_default=True
)

Typical file hierarchy:

• **FASTQ** (`output_type="reads"`): Raw sequencing reads with cell barcodes and UMIs
• **Gene quantifications** (`output_type="gene quantifications"`, format TSV): Count matrices (genes x cells) after ENCODE uniform pipeline processing
• **Filtered counts** (`output_type="filtered feature barcode matrix"`): Post-QC cell-filtered matrices ready for analys