CNV Inference (bioSkills)

A Claude Code skill that infers large-scale copy-number alterations from tumor single-cell or single-nucleus RNA-seq, so malignant cells can be told apart from normal ones without a matched DNA assay.

   
Type Claude Skill
Supplier GPTomics bioSkills (community OSS, MIT)
Availability GA — part of the bioSkills collection
Pricing Free / OSS (MIT) — inferCNV, copyKAT, Numbat and SCEVAN are separately installed OSS with their own licenses
Capabilities Read/Write — Claude runs the skill’s workflow locally (R), not as an MCP tool
Verified works · 2026-08-03
Security cleared · 2026-08-03 — GPTomics/bioSkills MIT confirmed, provenance matches, 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/cnv-inference ~/.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 chosen inference package on first use: BiocManager::install("infercnv"), or the GitHub sources for copyKAT, Numbat and SCEVAN.

What it does

Turns expression averaged over genomic windows into copy-number calls, then uses those calls to partition the dataset:

  • Method panel — inferCNV 1.18+ (reference-based, HMM-smoothed), copyKAT 1.1+ and SCEVAN 1.0+ (reference-free), and Numbat 1.4+ (allele-aware, combining expression with phased SNP evidence for subclone resolution).
  • Workflow — assemble a raw counts matrix, cell annotations, and a gene-order file with genomic coordinates; choose reference-based vs reference-free vs allele-aware; pick a normal reference (in-sample non-malignant cells preferred, sex-matched, with enough cells); smooth expression across genomic windows and apply the HMM for discrete states; call malignant cells by thresholding the CNV score or by subclustering, validating against lineage markers and allele evidence; refine subclones with Numbat where allelic information is available.
  • Parameter guidance — inferCNV’s cutoff is 0.1 for droplet data versus 1 for Smart-seq, because sparse droplet counts need a lower threshold; HMM_type i6 is the default six-state model.

Primary use cases: malignant-vs-normal cell separation in tumor scRNA-seq, aneuploidy and chromosome-arm CNV inference from expression, tumor subclone calling.

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-cnv-inference (tool_type: r, primary_tool: inferCNV); if invoked as a namespaced plugin command it resolves under the bioSkills plugin, not as a bare /cnv-inference. Expression-based CNV inference is an indirect measurement — a “CNV” signal can also arise from a strong regional expression program, which is why the skill insists on lineage-marker and allele cross-checks before a cell is labelled malignant. Reference-free methods avoid needing normal cells in the sample but are more sensitive to tumor purity. Pairs with Single-cell RNA QC upstream and cBioPortal or COSMIC for orthogonal bulk copy-number context. Upstream directory: single-cell/cnv-inference.

Sources

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