Cytometry QC (bioSkills)

A Claude Code skill that removes acquisition artifacts from flow, spectral and mass cytometry files before any gating or clustering — clogs, signal drift, boundary events and off-spec CyTOF acquisitions.

   
Type Claude Skill
Supplier GPTomics bioSkills (community OSS, MIT)
Availability GA — part of the bioSkills collection
Pricing Free / OSS (MIT) — flowAI, PeacoQC, flowCore, flowDensity and CATALYST 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/cytometry-qc ~/.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/cytometry-qc ~/.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("flowAI","PeacoQC","flowCore","flowDensity","CATALYST"))'

What it does

Enforces a QC order that most hand-rolled pipelines get wrong, then applies the time-series cleaners:

  • Order of operations — compensate/unmix → transform → margin removal (RemoveMargins()) → time-based QC → debris/doublet/dead-cell gating → batch normalization. Margins must precede any density-based step or the boundary pile-up creates spurious density ridges.
  • Time-based anomaly cleaning — flowAI (1.32+), PeacoQC (1.12+), flowCut and flowClean, targeting clogs, bubbles and monotonic signal drift.
  • Mass cytometry checks — Event_length window, bead-based sensitivity drift, retuning cadence.
  • Batch-level outlier flagging — per-sample summaries compared across a run to catch a single bad acquisition before it reaches differential testing.

Stated thresholds and defaults:

Parameter Value
PeacoQC MAD 6 (default; higher is less strict)
PeacoQC IT_limit 0.55 (default; higher is less strict)
flowClean minimum events ~30,000 — below this, CLR frequency tracking under-detects
CyTOF Event_length 10–75 (confirm per instrument)
Dead-cell fraction 10–30% is reported as a sample-handling flag, not auto-excluded
CyTOF retuning daily, or per long run — sensitivity decays from cone fouling

Primary use cases: pre-gating QC of FCS files, detecting clogs and signal drift, flagging outlier samples in a multi-batch immunophenotyping study.

Notes

The time parameter is the master QC axis: a missing or mis-scaled $TIMESTEP keyword silently breaks every time-based tool, so the skill checks it first. Dead-cell percentage is treated as metadata about sample handling rather than a filter threshold.

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

Sits first in the bioSkills flow-cytometry chain: Compensation and Transformation → this skill → Gating Analysis → Clustering and Phenotyping → Cytometry Differential Analysis. For reading and writing the FCS files themselves in Python, see FlowIO.

Sources

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