Data & ReproBioinformatics & GenomicsFreedomIntelligence/OpenClaw-Medical-SkillsData & Reproduction

bio-flow-cytometry-clustering-phenotyping

Maintainer FreedomIntelligence · Last updated April 1, 2026

Unsupervised clustering and cell type identification for flow/mass cytometry. Covers FlowSOM, Phenograph, and CATALYST workflows. Use when discovering cell populations in high-dimensional cytometry data without predefined gates.

OpenClawNanoClawAnalysisReproductionbio-flow-cytometry-clustering-phenotyping🧬 bioinformatics (gptomics bio-* suite)bioinformatics — immunoinformatics & flow cytometryunsupervised

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-flow-cytometry-clustering-phenotyping

Maintainer: FreedomIntelligence
License: MIT
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

R: FlowSOM::FlowSOM() for self-organizing map clustering.
R: CATALYST::cluster() with Phenograph or FlowSOM.
Cluster my cytometry data to find cell types" → Discover cell populations in high-dimensional flow/mass cytometry data using unsupervised clustering without predefined gates. R: FlowSOM::FlowSOM() for self-organizing map clustering R: CATALYST::cluster() with Phenograph or FlowSOM.
expr <- exprs(fcs) marker_cols <- grep('CD|HLA', colnames(fcs), value = TRUE).

Source Doc

Excerpt From SKILL.md

FlowSOM Clustering

Goal: Cluster cytometry events into cell populations using self-organizing maps.

Approach: Build a FlowSOM grid on marker channels, then extract metacluster assignments per cell.

library(FlowSOM)

## Build SOM

fsom <- FlowSOM(fcs,
                colsToUse = marker_cols,
                xdim = 10, ydim = 10,
                nClus = 20,
                seed = 42)

## Get cluster assignments

clusters <- GetMetaclusters(fsom)