bio-flow-cytometry-differential-analysis

FreedomIntelligence/OpenClaw-Medical-SkillsBiology Medicine and BioinformaticsAudit passedSnapshot c5426d886963

Summary

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.

SKILL.md

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)

# 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')

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')

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|)']
    ))
}

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)

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')

# 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)

# 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')
})

bio-flow-cytometry-differential-analysis

Summary

SKILL.md

Version Compatibility

Differential Analysis

Differential Abundance (DA)

Differential State (DS)

Visualization

Manual Statistical Testing

Mixed Effects Models

CITRUS (Automated Discovery)

Volcano Plot

Export Results

Multiple Comparisons

Related Skills

Related skills