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Agent skill

Bio Flow Cytometry Differential Analysis

by GPTomics in GPTomics/bioSkills

Differential abundance (DA) and differential state (DS) analysis for flow and mass cytometry - tests which cell populations change in frequency or marker expression between conditions using diffcyt…

MITAuto-check passed

Install Bio Flow Cytometry Differential Analysis

skills CLI
$ npx skills add GPTomics/bioSkills --skill bio-flow-cytometry-differential-analysis -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install GPTomics/bioSkills bio-flow-cytometry-differential-analysis --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/flow-cytometry/differential-analysis .claude/skills/bio-flow-cytometry-differential-analysis && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
bio-flow-cytometry-differential-analysis
GitHub stars
1.2k
Used in
1 other repo
Token cost
~2.2k tokens
SKILL.md length
824 words
Files
3
Skills in repo
559
Repo updated
First seen
Licence
MIT

At a glance

Differential abundance (DA) and differential state (DS) analysis for flow and mass cytometry - tests which cell populations change in frequency or marker expression between conditions using diffcyt…

  • Comparing populations between groups
  • SKILL.md covers Version Compatibility, The Single Most Important…, DA vs DS, and the type/state… and Method Taxonomy, plus 8 more sections
  • Runs R scripts from its folder
  • Choosing a DA method

What it does

Bio Flow Cytometry Differential Analysis is an agent skill from GPTomics/bioSkills. Differential abundance (DA) and differential state (DS) analysis for flow and mass cytometry - tests which cell populations change in frequency or marker expression between conditions using diffcyt (edgeR/voom/GLMM for DA, limma/LMM for DS), with cydar, CITRUS, and compositional methods (sccomp, scCODA, DCATS) as alternatives. Covers the sample-is-the-experimental-unit principle, design/contrast and mixed-model formulas, compositionality of cluster proportions, and FDR across clusters. Use when comparing…

Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `usage-guide.md`).

The repository describes itself as: a set of SKILLS.md for doing bioinformatics with agents like claude code. The licence is MIT.

When your agent uses it

  • Comparing populations between groups
  • Choosing a DA method
  • Handling paired/batch designs
  • Deciding whether compositional correction is needed

Example prompts

  • “/bio-flow-cytometry-differential-analysis”

What it can do on your machine

Read from SKILL.md and the folder at commit d91ed3d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (R), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Bio Flow Cytometry Differential Analysis loads about 2.2k tokens when it runs. Until then it costs about 172 tokens; SKILL.md has 824 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~172
When it runs · the whole SKILL.md, loaded when a task matches
~2.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from GPTomics/bioSkills at commit d91ed3d, republished under its MIT licence (© GPTomics). 824 words, ~2,230 tokens.

Download SKILL.mdSave it as .claude/skills/bio-flow-cytometry-differential-analysis/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
bio-flow-cytometry-differential-analysis
description
Differential abundance (DA) and differential state (DS) analysis for flow and mass cytometry - tests which cell populations change in frequency or marker expression between conditions using diffcyt (edgeR/voom/GLMM for DA, limma/LMM for DS), with cydar, CITRUS, and compositional methods (sccomp, scCODA, DCATS) as alternatives. Covers the sample-is-the-experimental-unit principle, design/contrast and mixed-model formulas, compositionality of cluster proportions, and FDR across clusters. Use when comparing populations between groups, choosing a DA method, handling paired/batch designs, or deciding whether compositional correction is needed.
tool_type
r
primary_tool
diffcyt

Version Compatibility

Reference examples tested with: diffcyt 1.22+, CATALYST 1.26+, edgeR 4.0+, limma 3.58+.

Before using code patterns, verify installed versions match. If versions differ:

  • R: packageVersion('<pkg>') then ?function_name to verify parameters

testDA_edgeR/testDS_limma are diffcyt functions operating on count/median objects from calcCounts/calcMedians; the CATALYST-integrated path is the diffcyt() wrapper on the SCE. Confirm the signature with ?diffcyt before relying on it.

Differential Analysis

"Compare cell populations between my conditions" -> Test cluster frequencies (DA) and within-cluster marker expression (DS) between groups, with the sample (not the cell) as the unit.

  • R: diffcyt::diffcyt(sce, analysis_type='DA', method_DA='diffcyt-DA-edgeR', design, contrast)
  • R: diffcyt(sce, analysis_type='DS', method_DS='diffcyt-DS-limma', ...)

The Single Most Important Modern Insight -- The Sample Is the Experimental Unit, Not the Cell

Tens of thousands of cells from one donor are technical PSEUDOREPLICATES, not independent observations. A per-cell test (Wilcoxon across all cells) treats them as n = cells and produces astronomically significant p-values from two mice - it is the single most common statistical sin in modern cytometry (Hurlbert 1984 Ecol Monogr 54:187; the cytometry mirror of the scRNA-seq pseudobulk lesson). The correct unit is the SAMPLE/subject: diffcyt aggregates cells to PER-SAMPLE-PER-CLUSTER counts (DA) and PER-SAMPLE-PER-CLUSTER arcsinh-MEDIANS (DS), then tests across samples with edgeR/limma/GLMM (Weber 2019 Commun Biol 2:183). Biological replication is mandatory (>= 2-3 per group); DA from a single sample per condition has no valid test. Paired with this: cluster proportions are COMPOSITIONAL (they sum to 1), so a real increase in one population mechanically forces apparent depletion in others - a source of false DA in "unchanged" clusters.

  • DA (differential abundance): does a cluster's FREQUENCY differ? Clusters are defined by TYPE markers.
  • DS (differential state): within a fixed-identity cluster, does a STATE marker's expression differ? State markers were withheld from clustering for exactly this test.

Method Taxonomy

MethodCitationMechanismWhen to use
diffcyt-DA-edgeR / voomWeber 2019 Commun Biol 2:183edgeR/voom empirical-Bayes on per-sample counts; optional TMMstandard 2+ group with replicates (DEFAULT)
diffcyt-DA-GLMM / DS-LMMWeber 2019random effects in the formulapaired/repeated-measures/nested (subject random effect)
cydarLun 2017 Nat Methods 14:707overlapping hyperspheres + edgeR + spatial FDRcontinuum, avoid hard clusters
CITRUSBruggner 2014 PNAS 111:E2770hierarchical clustering + LASSOpredictive signature, LARGE n; correlated-not-causal; largely superseded
sccomp / scCODA / DCATSMangiola 2023 PNAS 120:e2203828120 / Buttner 2021 Nat Commun 12:6876 / Lin 2023 Genome Biol 24:151simplex-aware compositional modelsstrong compositional shift (one pop dominates); DCATS for assignment uncertainty

Run diffcyt DA and DS

Goal: Test abundance and state on a CATALYST-clustered SCE.

Approach: Build design + contrast from ei(sce); the diffcyt() wrapper uses the stored clustering. State markers are tested in DS, type markers define DA clusters.

r
library(CATALYST); library(diffcyt)

sce <- readRDS('sce_clustered.rds')
design   <- createDesignMatrix(ei(sce), cols_design = 'condition')
contrast <- createContrast(c(0, 1))                    # Treatment vs Control

res_DA <- diffcyt(sce, clustering_to_use = 'meta20',
                  analysis_type = 'DA', method_DA = 'diffcyt-DA-edgeR',
                  design = design, contrast = contrast)
res_DS <- diffcyt(sce, clustering_to_use = 'meta20',
                  analysis_type = 'DS', method_DS = 'diffcyt-DS-limma',
                  design = design, contrast = contrast)

library(SummarizedExperiment)
rowData(res_DA$res)        # cluster_id, logFC, p_val, p_adj (BH across clusters)

Paired / Repeated-Measures (mixed models)

Goal: Account for within-subject correlation (e.g. pre/post on the same donor).

Approach: Use a GLMM/LMM method with a random effect for subject via a formula.

r
formula <- createFormula(ei(sce), cols_fixed = 'condition', cols_random = 'patient_id')
res_DA  <- diffcyt(sce, clustering_to_use = 'meta20',
                   analysis_type = 'DA', method_DA = 'diffcyt-DA-GLMM',
                   formula = formula, contrast = createContrast(c(0, 1)))

Compositional Re-Check

Goal: Confirm a headline single-population shift is not inducing artifactual reciprocal depletion.

Approach: Re-test with a simplex-aware model when one cluster changes a lot or total yield differs by group.

r
# If a dominant population expands, the apparent depletion of others may be a simplex artifact.
# Re-test with sccomp / scCODA (reference cell type) / DCATS (assignment uncertainty)
# before reporting reciprocal depletion as independent biology.
Show full SKILL.md (329 more words)Show less

Per-Method Failure Modes

Per-cell pseudoreplication

Trigger: Wilcoxon/t-test across all cells. Mechanism: cells aren't independent. Symptom: p ~ 1e-40 from few subjects. Fix: aggregate to per-sample summaries (diffcyt).

Compositional false DA

Trigger: one population expands strongly. Mechanism: proportions sum to 1. Symptom: significant "depletion" of unrelated clusters. Fix: TMM only when total cell abundance is NOT itself the biological signal (else it removes real signal), or a compositional method (sccomp/scCODA/DCATS); report total-yield differences.

Batch cleaned instead of modeled

Trigger: normalizing batch out then testing naively. Mechanism: over-correction removes real signal. Symptom: attenuated effects. Fix: include batch in the design; if batch == condition, no rescue - design it out.

No replicates

Trigger: 1 sample per condition. Mechanism: no error term. Symptom: uninterpretable p. Fix: require >= 2-3 biological replicates per group.

Quantitative Thresholds

ThresholdSourceRationale
>= 2-3 biological replicates per groupWeber 2019minimum for a valid DA/DS error term
BH FDR across clusters (and clusters x markers for DS)diffcythigh-resolution grids have many tests
arcsinh median as DS statisticNowicka 2017robust per-cluster per-sample summary

Common Errors

Error / symptomCauseSolution
testDA_edgeR(sce, ...) failswrong signatureuse the diffcyt() wrapper on the SCE, or calcCounts first
results emptywrong clustering_to_use namematch the stored clustering id (e.g. meta20)
no DS resultsstate markers not flaggedset marker_class='state' in the panel
paired design ignoredused fixed-effect methoduse diffcyt-DA-GLMM with a random effect

References

  • Weber 2019 Commun Biol 2:183 — diffcyt (DA + DS).
  • Bruggner 2014 PNAS 111(26):E2770-E2777 — CITRUS.
  • Lun 2017 Nat Methods 14(7):707-709 — cydar hypersphere DA.
  • Mangiola 2023 PNAS 120(33):e2203828120 — sccomp compositional analysis.
  • Buttner 2021 Nat Commun 12:6876 — scCODA.
  • Lin 2023 Genome Biol 24:151 — DCATS (assignment-uncertainty-aware).
  • Nowicka 2017 F1000Research 6:748 — CyTOF workflow; arcsinh-median DS statistic.
  • Hurlbert 1984 Ecol Monogr 54(2):187-211 — pseudoreplication.
  • clustering-phenotyping - Cluster (type markers) before testing
  • gating-analysis - Compare manually gated population frequencies
  • differential-expression/de-results - Shared edgeR/limma output semantics (padj)
  • differential-expression/edger-basics - The count-model engine diffcyt reuses
  • experimental-design/multiple-testing - FDR across clusters and clusters x markers
  • experimental-design/batch-design - Model batch in the design, don't clean it out

© GPTomics, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files in flow-cytometry/differential-analysis of GPTomics/bioSkills.

  • SKILL.md
  • examples/differential_abundance.R
  • usage-guide.md

Open the folder on GitHubat commit d91ed3d

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in GPTomics/bioSkills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Bio Flow Cytometry Differential Analysis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Bio Flow Cytometry Differential Analysis compared with similar skills
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Questions about Bio Flow Cytometry Differential Analysis

What does Bio Flow Cytometry Differential Analysis do?

Differential abundance (DA) and differential state (DS) analysis for flow and mass cytometry - tests which cell populations change in frequency or marker expression between conditions using diffcyt…. Bio Flow Cytometry Differential Analysis is an agent skill from GPTomics/bioSkills. Differential abundance (DA) and differential state (DS) analysis for flow and mass cytometry - tests which cell populations change in frequency or marker expression between conditions using diffcyt (edgeR/voom/GLMM for DA, limma/LMM for DS), with cydar, CITRUS, and compositional methods (sccomp, scCODA, DCATS) as alternatives.

When should I use Bio Flow Cytometry Differential Analysis?

Bio Flow Cytometry Differential Analysis fits situations like: comparing populations between groups; choosing a DA method; handling paired/batch designs; deciding whether compositional correction is needed.

How do I install Bio Flow Cytometry Differential Analysis in Claude Code?

Run `npx skills add GPTomics/bioSkills --skill bio-flow-cytometry-differential-analysis -a claude-code`. Or copy the skill folder (flow-cytometry/differential-analysis in GPTomics/bioSkills) into .claude/skills/bio-flow-cytometry-differential-analysis in your project. Claude Code loads it when a task matches its description.

How do I install Bio Flow Cytometry Differential Analysis in Codex?

Run `npx skills add GPTomics/bioSkills --skill bio-flow-cytometry-differential-analysis -a codex`. Or copy the skill folder (flow-cytometry/differential-analysis in GPTomics/bioSkills) into .agents/skills/bio-flow-cytometry-differential-analysis in your project. Codex loads it when a task matches its description.

Can I use Bio Flow Cytometry Differential Analysis in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add GPTomics/bioSkills --skill bio-flow-cytometry-differential-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bio-flow-cytometry-differential-analysis, .gemini/skills/bio-flow-cytometry-differential-analysis, .github/skills/bio-flow-cytometry-differential-analysis and .opencode/skills/bio-flow-cytometry-differential-analysis in your project.

What does Bio Flow Cytometry Differential Analysis need to run?

Going by SKILL.md and its folder, Bio Flow Cytometry Differential Analysis needs R for the scripts in its folder.

Does Bio Flow Cytometry Differential Analysis access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Bio Flow Cytometry Differential Analysis safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Bio Flow Cytometry Differential Analysis use?

Bio Flow Cytometry Differential Analysis is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Bio Flow Cytometry Differential Analysis use?

About 2.2k tokens (SKILL.md is roughly 8.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Bio Flow Cytometry Differential Analysis?

Skills that share tags, products or a category with Bio Flow Cytometry Differential Analysis: Bio Flow Cytometry Differential Analysis (FreedomIntelligence/OpenClaw-Medical-Skills, 3.1k stars), Bio Proteomics Differential Abundance (FreedomIntelligence/OpenClaw-Medical-Skills, 3.1k stars), Bio Microbiome Differential Abundance (FreedomIntelligence/OpenClaw-Medical-Skills, 3.1k stars) and Bio Imaging Mass Cytometry Data Preprocessing (FreedomIntelligence/OpenClaw-Medical-Skills, 3.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Bio Flow Cytometry Differential Analysis?

GPTomics (a GitHub organization) maintains it in GPTomics/bioSkills, which has 1,217 GitHub stars. The repository holds 559 skills in this directory. The repository was last updated on August 15, 2026.

Source: GPTomics/bioSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.