scATAC Analysis (bioSkills)

A Claude Code skill for single-cell ATAC-seq that covers fragment QC through consensus peak calling and transcription-factor motif scoring, with explicit guidance on the depth artefacts that dominate the assay.

   
Type Claude Skill
Supplier GPTomics bioSkills (community OSS, MIT)
Availability GA — part of the bioSkills collection
Pricing Free / OSS (MIT) — Signac, Seurat, ArchR, SnapATAC2, chromVAR, AMULET and TOBIAS are separately installed OSS
Capabilities Read/Write — Claude runs the skill’s workflow locally (R/Python), not as an MCP tool
Verified works · 2026-08-06
Security cleared · 2026-08-06 — GPTomics/bioSkills MIT, standard OSS deps, no advisories

How to install

bioSkills is not an npm package — skills are plain markdown/code read directly by the agent. Clone the repo, then either run the installer for the whole category or copy the single skill directory.

  • Claude Code — clone and install via the bundled script: 
    git clone https://github.com/GPTomics/bioSkills
cd bioSkills
./install-claude.sh --categories "single-cell"

    The installer copies matching skills into ~/.claude/skills/ (default target). Use ./install-claude.sh --list to preview the skills first.

  • Claude Code / other agents — copy just this one skill: 
    cp -r bioSkills/single-cell/scatac-analysis ~/.claude/skills/

    (run from inside your clone — the previous step left you in bioSkills/; otherwise replace bioSkills/ with the absolute path of your clone). Install the R stack on first use (Signac, Seurat, JASPAR2020, TFBSTools, motifmatchr, BSgenome.Hsapiens.UCSC.hg38), or pip install snapatac2 for the Python route.

What it does

Six workflow stages over a fragments file:

  1. Fragment QC and matrix creation — load fragments, compute TSS enrichment and nucleosome-signal metrics, filter cells.
  2. Dimensionality reduction — TF-IDF peak reweighting followed by truncated SVD (LSI), with a diagnostic step for the first component’s correlation with sequencing depth (usually discarded).
  3. Clustering and visualization — neighbor graph and UMAP on the retained LSI dimensions.
  4. Consensus peak calling — call peaks per cluster and merge into a single non-overlapping set, rather than using a whole-sample peak call that misses rare populations.
  5. Differential accessibility — logistic-regression testing with depth as a covariate.
  6. Motif analysis — chromVAR TF motif deviations scored against GC-matched background peak sets; TOBIAS for footprinting.

Framework choice — Signac 1.13+ with Seurat 5.0+ (R, the skill’s default), ArchR 1.0+ (R, arrow-file-backed for large atlases), or SnapATAC2 2.x (Python; API still evolving). Doublets are handled with AMULET and scDblFinder, and the skill distinguishes homotypic from heterotypic cases. It also takes a position on whether to binarize the count matrix.

Primary use cases: chromatin-accessibility cell typing, cluster-specific regulatory element discovery, TF activity inference from motif deviations.

Notes

Distributed as a SKILL.md (plus reference material) in the bioSkills collection — Claude executes the workflow locally rather than as an MCP server. The upstream skill front-matter name is bio-single-cell-scatac-analysis (tool_type: r, primary_tool: Signac); if invoked as a namespaced plugin command it resolves under the bioSkills plugin, not as a bare /scatac-analysis. Depth is the recurring confounder — it contaminates LSI component 1, biases differential accessibility, and inflates motif deviation scores if the background is not GC-matched, and the skill flags each of these separately. Peak-level and motif-level follow-up connects to MACS3, HOMER, JASPAR and deepTools. Upstream directory: single-cell/scatac-analysis.

Sources

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