bio-workflows-cytometry-pipeline
End-to-end flow cytometry workflow from FCS files to differential analysis. Orchestrates compensation, transformation, gating/clustering, and statistical testing with CATALYST/diffcyt. Use when processing flow or mass cytometry data end-to-end.
npx skills add majiayu000/claude-skill-registry --skill cytometry-pipeline-gptomics-bioskills-82c8ba96 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Flow Cytometry Pipeline ## Pipeline Overview ``` FCS Files ──> Compensation ──> Transformation ──> Gated/Clustered Data │ ▼ ┌─────────────────────────────────────────────────┐ │ cytometry-pipeline │ ├─────────────────────────────────────────────────┤ │ 1. Load FCS Files │ │ 2. Compensation & Transformation │ │ 3. QC & Filtering │ │ 4. Clustering (FlowSOM) or Gating │ │ 5. Dimensionality Reduction (UMAP) │ │ 6. Differential Abundance/State Analysis │ │ 7. Visualization │ └─────────────────────────────────────────────────┘ │ ▼ Differential Cell Populations + Markers ``` ## Complete R Workflow (CATALYST) ```r library(CATALYST) library(diffcyt) library(SingleCellExperiment) library(flowCore) library(ggplot2) # === 1. SETUP PANEL AND METADATA === # Panel definition 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') ) # Sample metadata md <- data.frame( file_name = list.files('data/', p
- Pipeline Overview
- Complete R Workflow (CATALYST)
- flowCore + Manual Gating Workflow
- Python Alternative (FlowCytometryTools)
- QC Checkpoints
- Workflow Variants
- CyTOF Data
- Paired Design
- Related Skills
What does the bio-workflows-cytometry-pipeline skill do?
End-to-end flow cytometry workflow from FCS files to differential analysis. Orchestrates compensation, transformation, gating/clustering, and statistical testing with CATALYST/diffcyt. Use when processing flow or mass cytometry data end-to-end.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill cytometry-pipeline-gptomics-bioskills-82c8ba96 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.