Cytometry Differential Analysis (bioSkills)

A Claude Code skill that compares cell populations between experimental groups in flow and mass cytometry without the per-cell pseudoreplication that inflates significance.

   
Type Claude Skill
Supplier GPTomics bioSkills (community OSS, MIT)
Availability GA — part of the bioSkills collection
Pricing Free / OSS (MIT) — diffcyt, CATALYST, edgeR and limma are separately installed Bioconductor packages
Capabilities Read/Write — Claude runs the skill’s R workflow locally, not as an MCP tool
Verified works · 2026-08-10
Security cleared · 2026-08-10 — MIT, provenance matches, bundled Bioconductor packages open source

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 "flow-cytometry"

    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/flow-cytometry/differential-analysis ~/.claude/skills/

    (run from inside the directory holding your clone — if you are still in bioSkills/ from the previous step, use cp -r flow-cytometry/differential-analysis ~/.claude/skills/, or replace bioSkills/ with the absolute path of your clone). Note the upstream directory is differential-analysis; this page is titled Cytometry Differential Analysis to distinguish it from the metagenomics Differential Abundance skill. Install the Bioconductor packages when prompted on first use:

    R -e 'BiocManager::install(c("diffcyt","CATALYST","edgeR","limma"))'

What it does

Splits the question into the two tests diffcyt distinguishes, and aggregates to the sample before testing either:

  • Differential abundance (DA) — per-sample-per-cluster cell counts tested with edgeR, voom, or a GLMM.
  • Differential state (DS) — per-sample-per-cluster arcsinh-median marker expression tested with limma or an LMM.
  • Design and contrast matrices — built from the experimental metadata (condition, patient, batch, paired timepoints).
  • Compositional validation — when a dominant population shifts, results are re-checked with simplex-aware methods (sccomp, scCODA, DCATS); cydar and CITRUS are covered as alternatives to cluster-based testing.

Stated rules:

Rule Value
Biological replicates per group ≥2–3, mandatory for a valid error term
Multiple testing Benjamini-Hochberg FDR across clusters and across cluster × marker combinations
DS summary statistic arcsinh-median per sample per cluster

Primary use cases: comparing immune-cell frequencies between patient groups, detecting activation-state changes within a population, treatment-vs-control CyTOF cohort analysis.

Notes

Four failure modes the skill guards against:

  • Per-cell pseudoreplication — a Wilcoxon or t-test across all cells treats cells as independent samples and manufactures significance. Aggregate to sample-level summaries first.
  • Compositional artifacts — one expanding population mechanically depletes the others’ proportions, so an apparent decrease may be arithmetic. Validate with simplex-aware methods.
  • Batch handling — model batch as a covariate in the design; do not normalize it out before testing.
  • No single-sample designs — a design with one sample per condition has no error term and cannot be tested.

Distributed as a SKILL.md (plus reference material) in the bioSkills collection — Claude executes the R workflow locally rather than as an MCP server. The upstream skill front-matter name is bio-flow-cytometry-differential-analysis; if invoked as a namespaced plugin command it resolves under the bioSkills plugin, not as a bare /differential-analysis. Upstream directory: flow-cytometry/differential-analysis.

Last step of the bioSkills flow-cytometry chain — it consumes clusters from Clustering and Phenotyping or populations from Gating Analysis, which in turn depend on Compensation and Transformation and Cytometry QC.

Sources

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