bio-flow-cytometry-differential-analysis - SKILL.md Agent Skill

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 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command

Differential Analysis

Differential Abundance (DA)

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 == 'Control')$freq
    treat <- filter(cluster_data, condition == 'Treatment')$freq

    if (length(ctrl) >= 2 && length(treat) >= 2) {
        test <- t.test(treat, ctrl)
        return(data.frame(
            cluster = cluster,
            fc = mean(treat) / mean(ctrl),
            pvalue = test$p.value
        ))
    }
    return(NULL)
}

results <- map_dfr(unique(freqs$cluster_id), ~test_abundance(freqs, .x))
results$padj <- p.adjust(results$pvalue, method = 'BH')

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 = c('gray', 'red')) +
    theme_bw() +
    labs(title = 'Differential Abundance')

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 = 'meta20')
})

Related Skills

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