Version Compatibility

Reference examples tested with: FlowSOM 2.10+, edgeR 4.0+, flowCore 2.14+, ggplot2 3.5+, limma 3.58+, numpy 1.26+, pandas 2.2+, scanpy 1.10+, scikit-learn 1.4+

Before using code patterns, verify installed versions match. If versions differ:

• Python:

  pip show <package>

  then

  help(module.function)

  to check signatures
• R:

  packageVersion('<pkg>')

  then

  ?function_name

  to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Flow Cytometry Pipeline

"Process my flow cytometry data from FCS to differential analysis" → Orchestrate compensation, transformation, doublet removal, FlowSOM clustering, phenotype annotation, and diffcyt differential testing across conditions.

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)

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/', 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')

# === 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]]))

# Prepare SingleCellExperiment with CATALYST
sce <- prepData(fs_comp, panel, md,
                transform = TRUE,
                cofactor = 5,  # For CyTOF use 5, flow cytometry use 150
                FACS = TRUE)

cat('Loaded', ncol(sce), 'cells\n')

# === 3. QC ===
# Per-sample cell counts
table(sce$sample_id)

# Expression distributions
plotExprs(sce, color_by = 'condition')
ggsave('qc_expression_distributions.png', width = 12, height = 8)

# MDS plot for sample similarity
plotMDS(sce, color_by = 'condition')
ggsave('qc_mds.png', width = 8, height = 6)

# === 4. CLUSTERING ===
cat('Clustering...\n')
sce <- cluster(sce,
               features = 'type',  # Use lineage markers
               xdim = 10, ydim = 10,
               maxK = 20,
               seed = 42)

# Metaclustering at different resolutions
table(cluster_ids(sce, 'meta20'))

# === 5. DIMENSIONALITY REDUCTION ===
cat('Running UMAP...\n')
sce <- runDR(sce, dr = 'UMAP', features = 'type')

# Plot UMAP
plotDR(sce, dr = 'UMAP', color_by = 'meta20')
ggsave('umap_clusters.png', width = 8, height = 6)

plotDR(sce, dr = 'UMAP', color_by = 'condition')
ggsave('umap_condition.png', width = 8, height = 6)

# === 6. CLUSTER ANNOTATION ===
# Heatmap of marker expression
plotExprHeatmap(sce, features = 'type', k = 'meta20',
                by = 'cluster_id', scale = 'last', bars = TRUE)
ggsave('heatmap_clusters.png', width = 12, height = 8)

# Manual annotation based on markers
cluster_annotations <- c(
    '1' = 'CD4 T cells',
    '2' = 'CD8 T cells',
    '3' = 'B cells',
    '4' = 'Monocytes',
    '5' = 'NK cells'
    # ... continue for all clusters
)
sce$cell_type <- cluster_annotations[cluster_ids(sce, 'meta20')]

# === 7. DIFFERENTIAL ANALYSIS ===
cat('Running differential analysis...\n')

# Create design matrix
design <- createDesignMatrix(ei(sce), cols_design = 'condition')

# Contrast
contrast <- createContrast(c(0, 1))  # Treatment vs Control

# Differential Abundance (DA)
res_DA <- testDA_edgeR(sce, design, contrast, cluster_id = 'meta20')

da_results <- as.data.frame(rowData(res_DA))
da_results <- da_results[order(da_results$p_adj), ]
cat('\nDifferential Abundance Results:\n')
print(da_results[, c('cluster_id', 'logFC', 'p_val', 'p_adj')])

# Differential State (DS) - marker expression
res_DS <- testDS_limma(sce, design, contrast,
                        cluster_id = 'meta20',
                        markers_include = rownames(sce)[rowData(sce)$marker_class == 'state'])

ds_results <- as.data.frame(rowData(res_DS))
cat('\nDifferential State Results:\n')
sig_ds <- ds_results[ds_results$p_adj < 0.05, ]
print(sig_ds[, c('cluster_id', 'marker_id', 'logFC', 'p_adj')])

# === 8. VISUALIZATION ===
# DA heatmap
plotDiffHeatmap(sce, res_DA, all = TRUE, fdr = 0.05)
ggsave('da_heatmap.png', width = 10, height = 8)

# Abundance boxplots
plotAbundances(sce, k = 'meta20', by = 'cluster_id', group_by = 'condition')
ggsave('abundance_boxplots.png', width = 12, height = 8)

# Volcano plot
da_results$significant <- da_results$p_adj < 0.05
ggplot(da_results, aes(x = logFC, y = -log10(p_adj), color = significant)) +
    geom_point(size = 3) +
    geom_hline(yintercept = -log10(0.05), linetype = 'dashed') +
    scale_color_manual(values = c('gray', 'red')) +
    theme_bw() +
    labs(title = 'Differential Abundance')
ggsave('da_volcano.png', width = 8, height = 6)

# === 9. EXPORT ===
write.csv(da_results, 'da_results.csv', row.names = FALSE)
write.csv(ds_results, 'ds_results.csv', row.names = FALSE)
saveRDS(sce, 'cytometry_analysis.rds')

cat('\nAnalysis complete!\n')
cat('Significant DA clusters:', sum(da_results$p_adj < 0.05), '\n')

flowCore + Manual Gating Workflow

library(flowCore)
library(flowWorkspace)
library(ggcyto)

# Load data
fs <- read.flowSet(list.files('data/', pattern = '\\.fcs$', full.names = TRUE))

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

# Transformation
trans <- estimateLogicle(fs_comp[[1]], colnames(comp_matrix))
fs_trans <- transform(fs_comp, trans)

# Create GatingSet
gs <- GatingSet(fs_trans)

# Apply gates
gs_add_gating_method(gs, alias = 'live',
                     pop = '+', parent = 'root',
                     dims = 'FSC-A,SSC-A',
                     gating_method = 'gate_flowclust_2d',
                     gating_args = list(K = 2, target = c(50000, 25000)))

gs_add_gating_method(gs, alias = 'singlets',
                     pop = '+', parent = 'live',
                     dims = 'FSC-A,FSC-H',
                     gating_method = 'singletGate')

# Visualize gates
autoplot(gs[[1]], 'singlets')

# Extract gated data
gated_data <- gs_pop_get_data(gs, 'singlets')

Python Alternative (FlowCytometryTools)

import flowkit as fk
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans

# Load FCS files
sample = fk.Sample('sample.fcs')

# Get data as DataFrame
data = sample.as_dataframe(source='raw')

# Compensation (if needed)
comp_matrix = sample.metadata['spill']
data_comp = np.dot(data, np.linalg.inv(comp_matrix))

# Arcsinh transformation
cofactor = 150  # For flow cytometry
data_trans = np.arcsinh(data_comp / cofactor)

# Clustering
scaler = StandardScaler()
data_scaled = scaler.fit_transform(data_trans)

kmeans = KMeans(n_clusters=10, random_state=42)
clusters = kmeans.fit_predict(data_scaled)

QC Checkpoints

Stage	Check	Action if Failed
Loading	All FCS files read	Check file integrity
Compensation	Spillover values reasonable	Recalculate
Transformation	Distributions normalized	Adjust cofactor
Events	>10K cells per sample	Check acquisition
Clustering	10-30 populations	Adjust K/resolution
DA	>3 replicates per group	Need more samples

Workflow Variants

CyTOF Data

# CyTOF-specific settings
sce <- prepData(fs, panel, md,
                transform = TRUE,
                cofactor = 5,  # CyTOF uses cofactor 5
                FACS = FALSE)  # Not flow cytometry

# Bead normalization should be done upstream (Fluidigm software)

Paired Design

# For paired samples (e.g., pre/post treatment)
design <- createDesignMatrix(ei(sce), cols_design = c('condition', 'patient_id'))

# Include patient as blocking factor
formula <- createFormula(ei(sce), cols_fixed = 'condition', cols_random = 'patient_id')
res_DA <- testDA_voom(sce, formula, contrast)

Related Skills

• flow-cytometry/fcs-handling - FCS file operations
• flow-cytometry/compensation-transformation - Data preprocessing
• flow-cytometry/gating-analysis - Manual gating
• flow-cytometry/clustering-phenotyping - Unsupervised clustering
• flow-cytometry/differential-analysis - Statistical testing
• flow-cytometry/doublet-detection - Remove doublet events
• flow-cytometry/bead-normalization - CyTOF EQ bead normalization
• flow-cytometry/cytometry-qc - Comprehensive QC
• single-cell/clustering - Related clustering methods