flow-cytometry-analysis

Flow Cytometry Analysis: Complete Analysis Pipeline

Overview

Flow Cytometry Analysis provides end-to-end computational workflows for analyzing flow cytometry data. Starting from FCS file parsing, through compensation matrix application, gating strategies (manual rectangular/polygon gates and automated Gaussian mixture model gating), to downstream analyses including immunophenotyping, CFSE proliferation tracking, cell cycle phase quantification (Dean-Jett-Fox model), and apoptosis assays (Annexin V/PI). This skill extends basic FCS file handling (flowio) with full analytical pipelines.

When to Use This Skill

• Analyzing multi-color flow cytometry experiments
• Applying compensation matrices to correct spectral overlap
• Building sequential gating hierarchies (debris exclusion, singlet gating, live/dead, marker gating)
• Immunophenotyping with multi-marker panels (CD3, CD4, CD8, etc.)
• Quantifying cell proliferation from CFSE/CellTrace dilution
• Determining cell cycle phase distribution from DNA content histograms
• Analyzing apoptosis from Annexin V / propidium iodide staining
• Creating density plots, histograms, and overlay visualizations
• Automated gating for high-throughput cytometry experiments

Related Skills: For raw FCS file parsing and creation use flowio. For single-cell RNA-seq analysis use scanpy.

Installation

uv pip install flowio fcsparser scipy numpy pandas matplotlib scikit-learn

Quick Start

from flowio import FlowData
import numpy as np

# Read FCS file
fcs = FlowData('sample.fcs')
events = fcs.as_array()
channels = fcs.pnn_labels

print(f"Events: {events.shape[0]}, Channels: {len(channels)}")
print(f"Channels: {channels}")

# Simple FSC/SSC gate (remove debris)
fsc_idx = channels.index('FSC-A')
ssc_idx = channels.index('SSC-A')
mask = (events[:, fsc_idx] > 50000) & (events[:, ssc_idx] > 10000)
gated = events[mask]
print(f"After gating: {gated.shape[0]} events ({100*mask.sum()/len(mask):.1f}%)")