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Agent skill

Bio Flow Cytometry Doublet Detection

by GPTomics in GPTomics/bioSkills

Detects and removes doublets/aggregates from flow, spectral, and mass cytometry before clustering or quantification.

MITAuto-check passed

Install Bio Flow Cytometry Doublet Detection

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

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

GitHub CLI
$ gh skill install GPTomics/bioSkills bio-flow-cytometry-doublet-detection --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/doublet-detection .claude/skills/bio-flow-cytometry-doublet-detection && 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-doublet-detection
GitHub stars
1.2k
Used in
1 other repo
Token cost
~2k tokens
SKILL.md length
752 words
Files
3
Skills in repo
559
Repo updated
First seen
Licence
MIT

At a glance

Detects and removes doublets/aggregates from flow, spectral, and mass cytometry before clustering or quantification.

  • Filtering aggregates before phenotyping
  • SKILL.md covers Version Compatibility, The Single Most Important…, Method Taxonomy and FSC-A vs FSC-H Singlet Gating…, plus 6 more sections
  • Runs R scripts from its folder
  • Choosing a doublet method for flow vs CyTOF

What it does

Bio Flow Cytometry Doublet Detection is an agent skill from GPTomics/bioSkills. Detects and removes doublets/aggregates from flow, spectral, and mass cytometry before clustering or quantification. Covers FSC-A vs FSC-H singlet discrimination (the Area-Height non-proportionality, not a 1D area gate), FSC-W/SSC width gating, CyTOF Gaussian discrimination parameters (Center/Offset/Width/Residual/Eventlength) and DNA intercalator gating, and the residual heterotypic conjugates that survive scatter gating and masquerade as double-positive populations. Use when filtering aggregates before…

Its SKILL.md is about 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

  • Filtering aggregates before phenotyping
  • Choosing a doublet method for flow vs CyTOF
  • Diagnosing a suspicious double-positive cluster

Example prompts

  • “Use the bio-flow-cytometry-doublet-detection skill to detect and removes doublets/aggregates from flow, spectral, and mass cytometry before…”
  • “/bio-flow-cytometry-doublet-detection”

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 Doublet Detection loads about 2k tokens when it runs. Until then it costs about 164 tokens; SKILL.md has 752 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~164
When it runs · the whole SKILL.md, loaded when a task matches
~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). 752 words, ~1,994 tokens.

Download SKILL.mdSave it as .claude/skills/bio-flow-cytometry-doublet-detection/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-doublet-detection
description
Detects and removes doublets/aggregates from flow, spectral, and mass cytometry before clustering or quantification. Covers FSC-A vs FSC-H singlet discrimination (the Area-Height non-proportionality, not a 1D area gate), FSC-W/SSC width gating, CyTOF Gaussian discrimination parameters (Center/Offset/Width/Residual/Event_length) and DNA intercalator gating, and the residual heterotypic conjugates that survive scatter gating and masquerade as double-positive populations. Use when filtering aggregates before phenotyping, choosing a doublet method for flow vs CyTOF, or diagnosing a suspicious double-positive cluster.
tool_type
r
primary_tool
flowCore

Version Compatibility

Reference examples tested with: flowCore 2.14+, CATALYST 1.26+, ggplot2 3.5+.

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

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

If code throws an error, introspect the installed package and adapt rather than retrying.

Doublet Detection

"Remove doublets from my cytometry data" -> Discriminate single cells from aggregates using pulse geometry (flow) or ion-cloud parameters (CyTOF), before any clustering or quantification.

  • R (flow/spectral): flowCore gate on the FSC-A vs FSC-H diagonal (+ FSC-W/SSC-W)
  • R (mass/CyTOF): gate on DNA intercalator + Gaussian/Event_length parameters

The Single Most Important Modern Insight -- Doublets Are Caught by Area-vs-Height Non-Proportionality, and Scatter Gating Is Necessary but Not Sufficient

A doublet has roughly double the pulse AREA of a singlet but NOT double the Height, and a longer Width/transit time - so singlets fall on a tight FSC-A vs FSC-H diagonal and doublets deflect above it. A 1D area histogram therefore does NOT remove doublets; the discriminating signal is the Area-Height relationship (plus Width). This matters because an unremoved doublet of a CD3+ and a CD19+ cell reads as an artifactual CD3+CD19+ "double-positive," and clustering will faithfully (and wrongly) carve it out as a real population. Crucially, scatter gating is necessary but NOT sufficient: heterotypic conjugates (e.g. a CD3+CD14+ T:monocyte) survive standard FSC-A/H gates and present as genuine double-positives whose lineage-marker levels look COMPARABLE to true single-positives - the tell is an ELEVATED shared marker (e.g. CD45) and a high bright-field aspect ratio, so the definitive resolver is imaging flow cytometry, not a lineage-intensity check (Stadinski 2020 Cytometry A 97:1102). On CyTOF there is no scatter at all - doublets are removed by ion-cloud Gaussian parameters and DNA intercalator content (Bagwell 2020 Cytometry A 97:184).

Method Taxonomy

MethodInstrumentPrincipleCaveat
FSC-A vs FSC-Hflow/spectralsinglets on the A-H diagonalthe standard; the discriminator is non-proportionality, not area
FSC-W / SSC-Wflow/spectraldoublets have longer pulse Widthcomplementary to A-vs-H
DNA intercalator (Ir191/193)CyTOFdoublets show ~2N+ DNAalso separates cells from beads/debris
Gaussian params + Event_lengthCyTOFion-cloud fit residual/length flags fusionscatches fusions DNA alone misses (Bagwell 2020)
imaging cytometryimaging flowbright-field aspect ratiothe only clean resolver of heterotypic conjugates

Note: cytometry doublet removal is GATING-based. DoubletFinder/Scrublet/scDblFinder are scRNA-seq DROPLET methods (they simulate artificial doublets) - limited transfer, because cytometry has direct physical doublet signals.

FSC-A vs FSC-H Singlet Gating (flow/spectral)

Goal: Keep events on the singlet diagonal.

Approach: A polygon along the A=H diagonal (preferred over a rectangle, which keeps off-diagonal doublets); visualize with the gate overlaid.

r
library(flowCore); library(ggcyto)

# matrix dimnames preserve 'FSC-A'/'FSC-H'; data.frame() would mangle them to FSC.A
singlet <- polygonGate(filterId = 'singlets', .gate = matrix(
  c(20000, 10000, 250000, 200000, 250000, 260000, 20000, 40000), ncol = 2, byrow = TRUE,
  dimnames = list(NULL, c('FSC-A', 'FSC-H'))))
singlets <- Subset(fs, singlet)
autoplot(fs[[1]], 'FSC-A', 'FSC-H') + ggcyto::geom_gate(singlet)

CyTOF Doublet Removal

Goal: Keep intercalator-positive single ion clouds.

Approach: Gate DNA intercalator (nucleated, ~2N) and Event_length/Gaussian residual; CATALYST exposes these as channels in the SCE.

r
library(CATALYST)
# prepData moves Time/Event_length to int_colData by default - keep them in the assay with FACS=TRUE
sce <- prepData(fs, panel, md, transform = TRUE, cofactor = 5, FACS = TRUE)
e <- assay(sce, 'exprs')

dna <- e['DNA1', ]                                   # intercalator-positive = nucleated single cells
keep <- dna > quantile(dna, 0.05) & dna < quantile(dna, 0.95)
if ('Event_length' %in% rownames(sce))               # retained by FACS=TRUE (now on the arcsinh scale)
  keep <- keep & e['Event_length', ] <= quantile(e['Event_length', ], 0.99)   # quantile-relative, so scale is fine
sce_singlets <- sce[, keep]

Per-Method Failure Modes

Show full SKILL.md (307 more words)Show less
1D area gate leaves doublets

Trigger: gating only FSC-A. Mechanism: doublets overlap singlets in area. Symptom: double-positive clusters persist. Fix: gate the FSC-A vs FSC-H diagonal (+ Width).

Heterotypic conjugate survives scatter gating

Trigger: a surprising double-positive between two single-positive clusters. Mechanism: T:monocyte conjugate is scatter-normal, lineage markers comparable to singlets. Symptom: "novel" DP population with an elevated shared marker (e.g. CD45). Fix: treat as suspected doublet; check the shared-marker signal; confirm/resolve by imaging flow (bright-field aspect ratio) when load-bearing.

CyTOF "doublet gate" using scatter

Trigger: porting flow logic to CyTOF. Mechanism: no FSC/SSC exists. Symptom: no scatter channels. Fix: use DNA + Gaussian/Event_length.

Quantitative Thresholds

ThresholdSourceRationale
expected doublet rate ~1-5% (PBMC), higher in tissuecommunityflag samples far above as prep issues - not a removal cutoff
Gaussian + DNA gating improves CV (3.45 -> ~2.04)Bagwell 2020 Cytometry A 97:184combined DNA + Gaussian over baseline (Gaussian alone ~2.41)

Note: a fixed "95th-percentile residual" cutoff is arbitrary; prefer a visual diagonal gate or the instrument's Gaussian parameters over an unjustified quantile.

Common Errors

Error / symptomCauseSolution
double-positive cluster that "shouldn't" existresidual heterotypic doubletscheck for an elevated shared marker (CD45); confirm by imaging flow
no FSC/SSC channels (CyTOF)mass data has no scatteruse DNA/Gaussian/Event_length
over-removal of large cellsrectangle gate clips real large singletsuse a diagonal polygon, not a box

References

  • Stadinski 2020 Cytometry A 97(11):1102-1104 — heterotypic doublets survive scatter gating.
  • Bagwell 2020 Cytometry A 97(2):184-198 — automated CyTOF cleanup via Gaussian/Event_length.
  • Finck 2013 Cytometry A 83(5):483-494 — CyTOF DNA/event parameters in normalization context.

Workflow order (CyTOF): EQ-bead drift normalization (raw, FIRST) -> cytometry-qc -> doublet-detection -> clustering -> CytoNorm cross-batch (LAST)

  • cytometry-qc - Run first: flow-rate/signal/margin cleaning
  • bead-normalization - CyTOF drift correction after doublet removal
  • fcs-handling - Load FCS files
  • gating-analysis - Where singlet discrimination sits in the hierarchy
  • clustering-phenotyping - Downstream analysis after doublet removal
  • single-cell/doublet-detection - Droplet scRNA-seq doublet methods (different principle)

© 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/doublet-detection of GPTomics/bioSkills.

  • SKILL.md
  • examples/detect_doublets.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

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Questions about Bio Flow Cytometry Doublet Detection

What does Bio Flow Cytometry Doublet Detection do?

Detects and removes doublets/aggregates from flow, spectral, and mass cytometry before clustering or quantification. Bio Flow Cytometry Doublet Detection is an agent skill from GPTomics/bioSkills. Detects and removes doublets/aggregates from flow, spectral, and mass cytometry before clustering or quantification.

When should I use Bio Flow Cytometry Doublet Detection?

Bio Flow Cytometry Doublet Detection fits situations like: filtering aggregates before phenotyping; choosing a doublet method for flow vs CyTOF; diagnosing a suspicious double-positive cluster.

How do I install Bio Flow Cytometry Doublet Detection in Claude Code?

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

How do I install Bio Flow Cytometry Doublet Detection in Codex?

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

Can I use Bio Flow Cytometry Doublet Detection 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-doublet-detection -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-doublet-detection, .gemini/skills/bio-flow-cytometry-doublet-detection, .github/skills/bio-flow-cytometry-doublet-detection and .opencode/skills/bio-flow-cytometry-doublet-detection in your project.

What does Bio Flow Cytometry Doublet Detection need to run?

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

Does Bio Flow Cytometry Doublet Detection 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 Doublet Detection 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 Doublet Detection use?

Bio Flow Cytometry Doublet Detection 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 Doublet Detection use?

About 2k tokens (SKILL.md is roughly 8k 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 Doublet Detection?

Skills that share tags, products or a category with Bio Flow Cytometry Doublet Detection: Bio Flow Cytometry Doublet Detection (FreedomIntelligence/OpenClaw-Medical-Skills, 3.1k stars), Bio Single Cell Doublet Detection (FreedomIntelligence/OpenClaw-Medical-Skills, 3.1k stars), Bio Imaging Mass Cytometry Data Preprocessing (FreedomIntelligence/OpenClaw-Medical-Skills, 3.1k stars) and Threat Detection (alirezarezvani/claude-skills, 28k 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 Doublet Detection?

GPTomics (a GitHub organization) maintains it in GPTomics/bioSkills, which has 1,218 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.