Gating Analysis (bioSkills)

A Claude Code skill that builds reproducible, scripted gating hierarchies for flow and spectral cytometry instead of hand-drawn gates that cannot be re-run.

   
Type Claude Skill
Supplier GPTomics bioSkills (community OSS, MIT)
Availability GA — part of the bioSkills collection
Pricing Free / OSS (MIT) — flowWorkspace, openCyto, flowDensity, flowCore and CytoML 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/gating-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/gating-analysis ~/.claude/skills/, or replace bioSkills/ with the absolute path of your clone). Install the Bioconductor packages when prompted on first use:

    R -e 'BiocManager::install(c("flowWorkspace","openCyto","flowDensity","flowCore","CytoML"))'

What it does

Organizes gates as a hierarchical GatingSet so the whole hierarchy is code and can be re-applied across samples:

  • Gate sequence — time filtering → debris removal on FSC/SSC → singlet detection on FSC-A vs FSC-H → viability gating → lineage classification. The order is a funnel; reordering bakes upstream artifacts into every downstream population.
  • Manual gates — rectangleGate, polygonGate, quadrant and boolean gates added with gs_pop_add() and applied with recompute().
  • Automated gating — openCyto CSV gating templates (mindensity, tailgate, quantileGate, gate_flowclust_2d) or flowDensity’s sequential data-driven bivariate density thresholds; flowClust for model-based gates.
  • FlowJo interoperability — workspaces read and written via CytoML, so an existing manual hierarchy can be imported and then applied programmatically.
  • Statistics — population counts and frequencies extracted with gs_pop_get_stats().

Stated thresholds:

Rule Value
Events needed for a coefficient of variation < 15% ~50–60 (Poisson floor for rare-event counting)
Cells to acquire for 1e-5 sensitivity ~1e6
Practical rare-event detection floor 1e-4 to 1e-5 frequency

Primary use cases: reproducible immunophenotyping hierarchies, automated gating across many samples, rare-event and MRD-style gating, importing and re-running FlowJo workspaces.

Notes

FMO controls, not isotype controls, set gate boundaries — spreading error is what sets the positive/negative edge (Roederer 2001); isotype controls only address nonspecific binding and place the boundary incorrectly.

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-gating-analysis; if invoked as a namespaced plugin command it resolves under the bioSkills plugin, not as a bare /gating-analysis. Upstream directory: flow-cytometry/gating-analysis.

Run after Compensation and Transformation and Cytometry QC. For high-parameter panels where a manual hierarchy is impractical, use Clustering and Phenotyping instead; either route feeds Cytometry Differential Analysis. For reading and writing FCS files in Python, see FlowIO.

Sources

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