bio-flow-cytometry-differential-analysis

name	bio-flow-cytometry-differential-analysis
description	Differential abundance and state analysis for cytometry data. Compare cell populations between conditions using statistical methods. Use when testing for significant changes in cell frequencies or marker expression between groups.
tool_type	r
primary_tool	CATALYST

Version Compatibility

Reference examples tested with: R stats (base), edgeR 4.0+, ggplot2 3.5+, limma 3.58+

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

• 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.

Differential Analysis

"Compare cell populations between my conditions" → Test for significant changes in cell type frequencies (differential abundance) or marker expression levels (differential state) between experimental groups.

• R: CATALYST::testDA_edgeR() or diffcyt::testDA_GLMM()

Differential Abundance (DA)

Goal: Test which cell population clusters differ in frequency between experimental conditions.

Approach: Create a design matrix and contrast from sample metadata, then run edgeR-based differential abundance testing on cluster counts per sample using testDA_edgeR from the diffcyt framework.

library(CATALYST)
library(diffcyt)

# Load clustered data
sce <- readRDS('sce_clustered.rds')

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

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

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

# View results
rowData(res_DA)$cluster_id
rowData(res_DA)$p_adj

# Significant clusters
sig_DA <- rowData(res_DA)$p_adj < 0.05
table(sig_DA)

Differential State (DS)

# Test for marker expression differences within clusters
res_DS <- testDS_limma(sce, design, contrast,
                        cluster_id = 'meta20',
                        markers_include = rownames(sce)[rowData(sce)$marker_class == 'state'])

# Results per marker per cluster
ds_results <- rowData(res_DS)

Visualization

# DA results heatmap
plotDiffHeatmap(sce, res_DA, all = TRUE, fdr = 0.05)

# DS results heatmap
plotDiffHeatmap(sce, res_DS, all = TRUE, fdr = 0.05)

# Abundance by condition
plotAbundances(sce, k = 'meta20', by = 'cluster_id', group_by = 'condition')

Manual Statistical Testing

library(tidyverse)

# Get cluster frequencies per sample
freqs <- colData(sce) %>%
    as.data.frame() %>%
    group_by(sample_id, condition, cluster_id = cluster_ids(sce, 'meta20')) %>%
    summarise(n = n(), .groups = 'drop') %>%
    group_by(sample_id) %>%
    mutate(freq = n / sum(n) * 100)

# Test each cluster
test_abundance <- function(df, cluster) {
    cluster_data <- filter(df, cluster_id == cluster)
    ctrl <- filter(cluster_data condition  freq
    treat  filtercluster_data condition  freq

     ctrl    treat   
        test  t.testtreat ctrl
        data.frame
            cluster  cluster
            fc  meantreat  meanctrl
            pvalue  testp.value
        
    
    


results  map_dfruniquefreqscluster_id test_abundancefreqs .x
resultspadj  p.adjustresultspvalue method  

Mixed Effects Models

library(lme4)
library(lmerTest)

# For paired/repeated measures designs
# Random effect for patient/donor

fit_mixed <- function(df, cluster) {
    cluster_data <- filter(df, cluster_id == cluster)

    model <- lmer(freq ~ condition + (1|patient_id), data = cluster_data)

    coef <- summary(model)$coefficients
    return(data.frame(
        cluster = cluster,
        estimate = coef[2, 'Estimate'],
        pvalue = coef[2, 'Pr(>|t|)']
    ))
}

CITRUS (Automated Discovery)

library(citrus)

# Prepare data
fcs_files <- list.files('data', pattern = '\\.fcs$', full.names = TRUE)
labels <- c(rep('Control', 2), rep('Treatment', 2))

# Run CITRUS
citrus_result <- citrus(
    fcs_files,
    labels,
    fileSampleSize = 1000,
    featureType = 'abundances',
    modelType = 'glmnet',
    family = 'classification'
)

# Get significant clusters
citrus_plot(citrus_result)

Volcano Plot

library(ggplot2)

# From DA results
da_df <- as.data.frame(rowData(res_DA))
da_df$significant <- da_df$p_adj < 0.05

ggplot(da_df, aes(x = logFC, y = -log10(p_adj), color = significant)) +
    geom_point() +
    geom_hline(yintercept = -log10(0.05), linetype = 'dashed') +
    geom_vline(xintercept = c(-1, 1), linetype = 'dashed') +
    scale_color_manual(values    
    theme_bw 
    labstitle  

Export Results

# Combine DA and DS results
da_results <- as.data.frame(rowData(res_DA))
da_results$analysis <- 'DA'

ds_results <- as.data.frame(rowData(res_DS))
ds_results$analysis <- 'DS'

# Save
write.csv(da_results, 'da_results.csv', row.names = FALSE)
write.csv(ds_results, 'ds_results.csv', row.names = FALSE)

Multiple Comparisons

# For multiple conditions
design_full <- model.matrix(~ 0 + condition, data = ei(sce))
colnames(design_full) <- levels(factor(ei(sce)$condition))

# Multiple contrasts
contrasts <- makeContrasts(
    TreatA_vs_Ctrl = TreatmentA - Control,
    TreatB_vs_Ctrl = TreatmentB - Control,
    TreatA_vs_B = TreatmentA - TreatmentB,
    levels = design_full
)

# Test each contrast
res_list <- lapply(1:ncol(contrasts), function(i) {
    testDA_edgeR(sce, design_full, contrasts[, i], cluster_id  

Related Skills

• clustering-phenotyping - Cluster data first
• gating-analysis - Compare gated populations
• differential-expression/de-results - Similar statistical concepts