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

bio-workflows-cytometry-pipeline

Maintainer FreedomIntelligence · Last updated April 1, 2026

Flow/mass cytometry: QC → gating → clustering → differential.

OpenClawNanoClawAnalysisReproductionbio-workflows-cytometry-pipeline🧠 bioos extended suitebioos extended bioinformatics suiteflow

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-workflows-cytometry-pipeline

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

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

panel <- data.frame( fcs_colname = c('FSC-A', 'SSC-A', 'CD45', 'CD3', 'CD4', 'CD8', 'CD19', 'CD14', 'CD56', 'HLA-DR', 'Ki67', 'IFNg'), antigen = c('FSC', 'SSC', 'CD45', 'CD3', 'CD4', 'CD8', 'CD19', 'CD14', 'CD56', 'HLA-DR', 'Ki67', 'IFNg'), marker_class = c('none', 'none', 'type', 'type', 'type', 'type', 'type', 'type', 'type', 'type', 'state', 'state') ).
md <- data.frame( file_name = list.files('data/', pattern = '\\.fcs$'), sample_id = paste0('Sample', 1:8), condition = rep(c('Control', 'Treatment'), each = 4), patient_id = rep(paste0('Patient', 1:4), 2) ).
cat('Loading', nrow(md), 'FCS files...\n').

Source Doc

Excerpt From SKILL.md

Complete R Workflow (CATALYST)

library(CATALYST)
library(diffcyt)
library(SingleCellExperiment)
library(flowCore)
library(ggplot2)

## === 2. LOAD AND PREPARE DATA ===

fcs_files <- file.path('data', md$file_name)
fs <- read.flowSet(fcs_files)

## Apply compensation if stored in FCS

fs_comp <- compensate(fs, spillover(fs[[1]]))

Use cases

Use bio-workflows-cytometry-pipeline for genomics and bioinformatics workflows.
Apply bio-workflows-cytometry-pipeline to sequencing, variant, or omics analysis tasks.