572 Usage Guide 350ebe17

Flow Cytometry Pipeline Usage Guide

Overview

This workflow processes flow cytometry data from raw FCS files through compensation, transformation, clustering or gating, and differential analysis.

Prerequisites

BiocManager::install(c('CATALYST', 'flowCore', 'diffcyt'))
install.packages(c('FlowSOM', 'uwot'))

Quick Start

Tell your AI agent what you want to do:

• "Run the cytometry pipeline on my FCS files"
• "Cluster my CyTOF data and find differential populations"
• "Analyze my immunophenotyping experiment"

Example Prompts

Basic Analysis

"I have FCS files from a CyTOF experiment, run the full pipeline"

"Cluster my flow cytometry data with FlowSOM"

Differential Analysis

"Compare immune populations between treatment and control groups"

"Find differentially abundant cell types in my flow data"

Customization

"Use manual gating instead of clustering for my data"

"Run differential state analysis on my T cell markers"

When to Use This Pipeline

• Multi-color flow cytometry panels
• CyTOF/mass cytometry experiments
• Immunophenotyping studies
• Treatment response analysis
• Biomarker discovery

Required Inputs

1. FCS files - One per sample
2. Panel file - Channel to marker mapping with marker classes
3. Sample metadata - Conditions, patient IDs, batches

Panel File Format

fcs_colname,antigen,marker_class
FSC-A,FSC,none
SSC-A,SSC,none
CD45,CD45,type
CD3,CD3,type
CD4,CD4,type
Ki67,Ki67,state
IFNg,IFNg,state

Marker classes:

• type: Lineage markers for clustering/phenotyping
• state: Functional markers for differential state analysis
• none: Scatter or non-analyzed channels

Pipeline Steps

1. Data Loading

• Read FCS files into R
• Validate panel and metadata

2. Compensation

• Apply spillover matrix
• Essential for fluorescence-based flow
• Not needed for CyTOF

3. Transformation

• Arcsinh transformation (standard)
• Cofactor: 5 for CyTOF, 150 for flow
• Normalizes distributions

4. QC

• Check cell counts per sample
• Visualize expression distributions
• Identify outlier samples

5. Clustering or Gating

• Clustering: FlowSOM, Phenograph (unsupervised)
• Gating: Manual population identification

6. Dimensionality Reduction

• UMAP for visualization
• Based on lineage markers

7. Differential Analysis

• DA: Are population proportions different?
• DS: Is marker expression different within populations?

Analysis Approaches

Approach 1: Unsupervised Clustering

Best for: High-dimensional panels, discovery

1. Cluster on lineage markers
2. Annotate clusters based on expression
3. Test differential abundance

Approach 2: Manual Gating

Best for: Well-defined populations, lower dimensions

1. Gate populations hierarchically
2. Export gated populations
3. Compare frequencies

Cofactor Guidelines

Statistical Considerations

Differential Abundance

• Need biological replicates (n ≥ 3)
• Uses edgeR (count-based)
• Reports fold change in frequency

Differential State

• Tests marker expression within clusters
• Uses limma
• Adjust for multiple testing (clusters × markers)

Common Issues

Compensation problems

• Check single-stain controls
• Verify spillover matrix
• Look for negative values

Poor clustering

• Adjust number of clusters (K)
• Check if markers are appropriate
• Try different resolution

No significant results

• Check sample size (n ≥ 3)
• Verify biological effect expected
• Adjust thresholds carefully

Output Files

Tips

• Panel file: Essential for correct channel-to-marker mapping
• Cofactor: Use 5 for CyTOF, 150 for conventional flow
• Batch effects: Include batch in metadata if samples were processed separately
• Replicates: Minimum 3 biological replicates per condition for statistical testing
• Marker classes: Separate lineage (type) from functional (state) markers

References

• CATALYST: doi:10.1101/218826
• diffcyt: doi:10.1038/s41467-017-00707-4
• FlowSOM: doi:10.1002/cyto.a.22625