Flow Cytometry Pipeline
Pipeline Overview
FCS Files ──> Compensation ──> Transformation ──> Gated/Clustered Data
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┌─────────────────────────────────────────────────┐
│ 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 │
└─────────────────────────────────────────────────┘
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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
FridrichMethod