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Agent skill

Bio Flow Cytometry Bead Normalization

by GPTomics in GPTomics/bioSkills

Bead-based signal normalization and cross-batch harmonization for CyTOF and high-parameter cytometry - EQ four-element bead normalization of instrument sensitivity drift (CATALYST normCytof…

MITAuto-check passedDatabases

Install Bio Flow Cytometry Bead Normalization

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

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

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

At a glance

Bead-based signal normalization and cross-batch harmonization for CyTOF and high-parameter cytometry - EQ four-element bead normalization of instrument sensitivity drift (CATALYST normCytof…

  • Correcting CyTOF signal drift
  • SKILL.md covers Version Compatibility, The Single Most Important…, Why Per-Cluster and Many (99)… and EQ-Bead Normalization (drift), plus 6 more sections
  • Runs R scripts from its folder
  • Harmonizing multi-batch

What it does

Bio Flow Cytometry Bead Normalization is an agent skill from GPTomics/bioSkills. Bead-based signal normalization and cross-batch harmonization for CyTOF and high-parameter cytometry - EQ four-element bead normalization of instrument sensitivity drift (CATALYST normCytof, premessa), and reference-anchor cross-batch normalization (CytoNorm, per-cluster quantile splines). Covers the distinction between within-run drift correction and between-batch correction, the mandatory anchor/reference sample, why normalization is per-cluster with many quantiles, and the over-correction risk. Use when…

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`).

It sits in Databases, covering Database schema design. 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

  • Correcting CyTOF signal drift
  • Harmonizing multi-batch
  • Multi-site studies
  • Deciding whether to normalize data versus model batch in the design

Example prompts

  • “/bio-flow-cytometry-bead-normalization”

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 Bead Normalization loads about 2k tokens when it runs. Until then it costs about 175 tokens; SKILL.md has 738 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~175
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). 738 words, ~2,005 tokens.

Download SKILL.mdSave it as .claude/skills/bio-flow-cytometry-bead-normalization/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-bead-normalization
description
Bead-based signal normalization and cross-batch harmonization for CyTOF and high-parameter cytometry - EQ four-element bead normalization of instrument sensitivity drift (CATALYST normCytof, premessa), and reference-anchor cross-batch normalization (CytoNorm, per-cluster quantile splines). Covers the distinction between within-run drift correction and between-batch correction, the mandatory anchor/reference sample, why normalization is per-cluster with many quantiles, and the over-correction risk. Use when correcting CyTOF signal drift, harmonizing multi-batch or multi-site studies, or deciding whether to normalize data versus model batch in the design.
tool_type
r
primary_tool
CATALYST

Version Compatibility

Reference examples tested with: CATALYST 1.26+, CytoNorm 2.0+, flowCore 2.14+.

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

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

normCytof() returns a LIST ($data, $beads, $removed, ...), not a flowFrame; beads="dvs" encodes EQ masses 140,151,153,165,175. Confirm with ?normCytof before relying on slot names.

Bead Normalization

"Normalize my CyTOF data" -> Correct instrument sensitivity drift with EQ beads (within/across runs), then harmonize batches with a reference anchor.

  • R (drift): CATALYST::normCytof() (EQ-bead-based) or premessa
  • R (batch): CytoNorm::CytoNorm.train() + CytoNorm.normalize() (per-cluster quantile splines)

The Single Most Important Modern Insight -- Two Different Layers; Anchor Controls Are the Guarantee

Bead normalization and batch normalization correct DIFFERENT things and are NOT interchangeable. (1) EQ-BEAD normalization (Finck 2013 Cytometry A 83:483) corrects within-run and run-to-run instrument SENSITIVITY DRIFT using the four-element beads as a physical internal standard - applied first, on raw counts. (2) CROSS-BATCH normalization (CytoNorm, Van Gassen 2020 Cytometry A 97:268) corrects staining/acquisition batch effects using a shared ANCHOR/reference sample present in EVERY batch, learning per-FlowSOM-cluster quantile-spline transforms. Beads cannot fix staining-batch or reagent-lot effects; CytoNorm cannot fix intra-run detector drift. The anchor control is the load-bearing design element: because it is biologically identical across batches, any cross-batch difference in it is technical BY CONSTRUCTION. Dropping the anchor (CytoNorm 2.0) is convenient but reintroduces the over-correction risk the anchor was designed to eliminate - so the safest stance for inference is to MODEL batch in the diffcyt design and reserve normalization for visualization/clustering display.

Why Per-Cluster and Many (99) Quantiles

Batch effects are cell-type-specific - a marker can drift in monocytes but not in T cells - so a single global channel transform over-corrects one population while under-correcting another and can erase real abundance differences. CytoNorm therefore learns the transform PER FlowSOM cluster. And it uses ~99 quantiles + a spline because the drift is non-linear and intensity-dependent (the negative and positive peaks move by different amounts); a single median shift or linear rescale reintroduces the distortion it is trying to remove.

EQ-Bead Normalization (drift)

Goal: Correct sensitivity drift and remove bead events.

Approach: normCytof() gates beads, computes the correction on the linear scale, and returns a list - the cleaned SCE is in $data.

r
library(CATALYST)

sce <- prepData(fs, panel, md)                         # no by_time arg - normalization is normCytof's job
res <- normCytof(sce, beads = 'dvs',                   # EQ masses 140,151,153,165,175
                 k = 500, remove_beads = TRUE, overwrite = FALSE)   # k = smoothing window (default; affects bead-trace viz, not correction magnitude)
sce_norm <- res$data                                   # normalized SCE; res$beads / res$removed available

Cross-Batch Normalization (CytoNorm)

Goal: Harmonize batches using a shared reference sample.

Approach: Train on the anchor (present in every batch) -> learn per-cluster quantile splines -> apply to the real samples. testCV() first: if cluster CV is high, the FlowSOM model is batch-unstable and per-cluster splines will distort (fall back to nClus=1).

r
library(CytoNorm)

model <- CytoNorm.train(files = ref_files, labels = batch_labels, channels = marker_channels,
                        transformList = tl,
                        FlowSOM.params = list(nCells = 6000, xdim = 10, ydim = 10, nClus = 10),
                        normMethod.train = QuantileNorm.train,
                        normParams = list(nQ = 99), seed = 42)
CytoNorm.normalize(model = model, files = sample_files, labels = batch_labels,
                   transformList = tl, transformList.reverse = tl_rev,   # BOTH required
                   outputDir = 'normalized/')

Per-Method Failure Modes

Treating bead and batch normalization as the same

Trigger: expecting beads to fix staining-batch effects. Mechanism: different layers. Symptom: residual batch structure after bead norm. Fix: bead norm for drift; CytoNorm for batch.

Show full SKILL.md (290 more words)Show less
No anchor in a batch

Trigger: a batch lacking the reference sample. Mechanism: nothing biologically-identical to learn from. Symptom: that batch can't be normalized / is over-corrected. Fix: run the anchor in every batch (or model batch instead).

Over-correction

Trigger: CytoNorm with groups confounded with batch, or anchor-free on variable samples. Mechanism: splines absorb real biology. Symptom: attenuated group differences. Fix: testCV() check; model batch in diffcyt for inference; normalize for display only.

Using normCytof return as a flowFrame

Trigger: sce_norm <- normCytof(...). Mechanism: it returns a list. Symptom: downstream type error. Fix: res$data.

Quantitative Thresholds

ThresholdSourceRationale
bead drift reduced ~4.9x -> 1.3xFinck 2013 Cytometry A 83:483EQ-bead correction over a month of runs
99 quantiles, per-clusterVan Gassen 2020 Cytometry A 97:268non-linear intensity-dependent, cell-type-specific drift
EQ masses 140,151,153,165,175 (dvs)CATALYSTDVS/Fluidigm EQ four-element bead set

Common Errors

Error / symptomCauseSolution
normCytof output not usableit returns a listuse res$data
prepData(by_time=TRUE) errorsno such argumentuse normCytof() for bead/drift correction
CytoNorm distorts populationsunstable FlowSOM clusteringrun testCV(); reduce nClus (or 1)
batch effect remainsonly bead-normalizedadd CytoNorm with anchor samples

References

  • Finck 2013 Cytometry A 83(5):483-494 — EQ-bead normalization of CyTOF drift.
  • Van Gassen 2020 Cytometry A 97(3):268-278 — CytoNorm per-cluster quantile normalization.
  • Quintelier 2025 Cytometry A 107(2):69-87 — CytoNorm 2.0 (anchor-free; over-correction caveat).
  • Chevrier 2018 Cell Syst 6(5):612-620 — CyTOF spillover (CATALYST normalization context).

Workflow order (CyTOF): EQ-bead drift normalization (raw counts, FIRST) -> cytometry-qc -> doublet-detection -> clustering -> CytoNorm cross-batch (LAST). The two normalization layers sit at opposite ends.

  • cytometry-qc - EQ-bead-median-vs-Time is the primary CyTOF drift readout
  • doublet-detection - Remove doublets before normalization
  • compensation-transformation - Transform scale used by CytoNorm
  • clustering-phenotyping - Cluster across normalized batches
  • differential-analysis - Model batch in the design rather than over-cleaning
  • experimental-design/batch-design - Anchor/reference-sample design; differential-expression/batch-correction for execution

© 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/bead-normalization of GPTomics/bioSkills.

  • SKILL.md
  • examples/bead_normalize.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.

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Categories

Questions about Bio Flow Cytometry Bead Normalization

What does Bio Flow Cytometry Bead Normalization do?

Bead-based signal normalization and cross-batch harmonization for CyTOF and high-parameter cytometry - EQ four-element bead normalization of instrument sensitivity drift (CATALYST normCytof…. Bio Flow Cytometry Bead Normalization is an agent skill from GPTomics/bioSkills. Bead-based signal normalization and cross-batch harmonization for CyTOF and high-parameter cytometry - EQ four-element bead normalization of instrument sensitivity drift (CATALYST normCytof, premessa), and reference-anchor cross-batch normalization (CytoNorm, per-cluster quantile splines).

When should I use Bio Flow Cytometry Bead Normalization?

Bio Flow Cytometry Bead Normalization fits situations like: correcting CyTOF signal drift; harmonizing multi-batch; multi-site studies; deciding whether to normalize data versus model batch in the design.

How do I install Bio Flow Cytometry Bead Normalization in Claude Code?

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

How do I install Bio Flow Cytometry Bead Normalization in Codex?

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

Can I use Bio Flow Cytometry Bead Normalization 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-bead-normalization -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-bead-normalization, .gemini/skills/bio-flow-cytometry-bead-normalization, .github/skills/bio-flow-cytometry-bead-normalization and .opencode/skills/bio-flow-cytometry-bead-normalization in your project.

What does Bio Flow Cytometry Bead Normalization need to run?

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

Does Bio Flow Cytometry Bead Normalization 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 Bead Normalization 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 Bead Normalization use?

Bio Flow Cytometry Bead Normalization 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 Bead Normalization 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 Bead Normalization?

Skills that share tags, products or a category with Bio Flow Cytometry Bead Normalization: SQL Optimization Patterns (ynulihao/AgentSkillOS, 617 stars), Datamodellm (nimbalyst/nimbalyst, 1.9k stars), Add Mpk Task (mirage-project/mirage, 2.5k stars) and B200 Flash Attention4 Planner (mirage-project/mirage, 2.5k 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 Bead Normalization?

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.