bio-flow-cytometry-doublet-detection

Version Compatibility

Reference examples tested with: flowCore 2.14+, ggplot2 3.5+

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.

Doublet Detection

"Remove doublets from my flow cytometry data" → Detect and filter out cell aggregates using FSC-A/FSC-H gating or computational methods before clustering or quantitative analysis.

• R: flowCore rectangular gates on FSC-A vs FSC-H

FSC-A vs FSC-H Gating (Standard Method)

library(flowCore)
library(ggcyto)

# Load data
fs <- read.flowSet(list.files('data/', pattern = '\\.fcs
#x27;, full.names = TRUE))

# FSC-A vs FSC-H for doublet discrimination
# Singlets fall on diagonal, doublets have higher FSC-A for given FSC-H

# Manual rectangular gate
singlet_gate <- rectangleGate(
    filterId = 'singlets',
    'FSC-A' = c(50000, 250000),
    'FSC-H' = c(50000, 250000)
)

# Or use polygon gate for diagonal
singlet_polygon <- polygonGate(
    filterId = 'singlets',
    .gate = data.frame(
        'FSC-A' = c(50000, 250000, 250000, 50000),
        'FSC-H' = c(40000, 200000, 260000, 60000)
    )
)

# Apply gate
singlets <- Subset(fs, singlet_gate)

# Visualize
autoplot(fs[[1]], 'FSC-A', 'FSC-H') + geom_gate(singlet_gate)

Automated Singlet Gating with flowDensity

library(flowDensity)

# Automatic singlet gate
singlet_result <- flowDensity(
    fs[[1]],
    channels = c('FSC-A', 'FSC-H'),
    position = c(TRUE, TRUE),
    gates = c(NA, NA)
)

# Get gated population
singlets <- getflowFrame(singlet_result)

# Percentage singlets
pct_singlets <- nrow(singlets) / nrow(fs[[1]]) * 100
cat('Singlets:', round(pct_singlets, 1), '%\n')

flowAI Quality Control

library(flowAI)

# flowAI performs comprehensive QC including:
# - Flow rate anomaly detection
# - Signal acquisition anomaly detection
# - Dynamic range anomaly detection

# Run flowAI
fs_qc <- flow_auto_qc(
    fs,
    folder_results = 'flowAI_results',
    fcs_QC = TRUE,
    fcs_highQ = TRUE
)

# Results include singlet detection based on flow rate stability

FSC-A/FSC-W Method (Width Parameter)

# Some instruments provide FSC-W (width) instead of FSC-H
# FSC-A = FSC-H × FSC-W
# Doublets have higher width

if ('FSC-W' %in% colnames(fs[[1]])) {
    singlet_gate_w <- rectangleGate(
        filterId = 'singlets',
        'FSC-A' = c(50000, 250000),
        'FSC-W' = c(50000, 100000)  # Lower width = singlets
    )

    singlets <- Subset(fs, singlet_gate_w)
}

Ratio-Based Doublet Detection

# Calculate FSC-A/FSC-H ratio
# Singlets have ratio close to constant (based on pulse geometry)
# Doublets have elevated ratio

calculate_fsc_ratio <- function(ff) {
    fsc_a <- exprs(ff)[, 'FSC-A']
    fsc_h <- exprs(ff)[, 'FSC-H']

    ratio <- fsc_a / (fsc_h + 1)  # Add small value to avoid division by zero
    return(ratio)
}

# Add ratio as derived parameter
for (i in 1:length(fs)) {
    ratio <- calculate_fsc_ratio(fs[[i]])
    fs[[i]] <- cbind2(fs[[i]], ratio)
    colnames(fs[[i]])[ncol(fs[[i]])] <- 'FSC_ratio'
}

# Gate on ratio
ratio_cutoff <- quantile(exprs(fs[[1]])[, 'FSC_ratio'], 0.95)
singlet_gate_ratio <- rectangleGate(filterId = 'singlets', 'FSC_ratio' = c(0, ratio_cutoff))

SSC-Based Doublet Detection

# For cell types where FSC doesn't discriminate well,
# use SSC-A vs SSC-H additionally

ssc_singlet_gate <- rectangleGate(
    filterId = 'ssc_singlets',
    'SSC-A' = c(10000, 200000),
    'SSC-H' = c(10000, 200000)
)

# Combine FSC and SSC gates
combined_gate <- singlet_gate & ssc_singlet_gate
singlets <- Subset(fs, combined_gate)

CyTOF Doublet Detection

library(CATALYST)

# For CyTOF data, use DNA channels or event length

# DNA-based doublet detection (if DNA channels present)
# Doublets have ~2x DNA content
sce <- prepData(fs, panel, md)

# If Event_length channel exists
if ('Event_length' %in% rownames(sce)) {
    event_length <- assay(sce)['Event_length', ]
    singlet_idx <- event_length < quantile(event_length, 0.95)

    sce_singlets <- sce[, singlet_idx]
    cat('Removed', sum(!singlet_idx), 'doublets based on event length\n')
}

# DNA intercalator method
if (all(c('DNA1', 'DNA2') %in% rownames(sce))) {
    dna_total <- assay(sce)['DNA1', ] + assay(sce)['DNA2', ]
    dna_cutoff <- quantile(dna_total, 0.95)

    singlet_idx <- dna_total < dna_cutoff
    sce_singlets <- sce[, singlet_idx]
}

CATALYST Workflow with Doublet Removal

Goal: Detect and remove cell doublets from a CyTOF/flow dataset using a regression-based approach on scatter parameters.

Approach: Model the expected FSC-A vs FSC-H relationship for singlets with linear regression, classify events with large residuals (above the 95th percentile) as doublets, and filter them out.

library(CATALYST)

# Load and prepare data
sce <- prepData(fs, panel, md, transform = TRUE, cofactor = 5)

# Remove doublets using marker-based method
sce <- filterSCE(sce, !is_doublet(sce))

# Custom doublet detection based on FSC
fsc_a <- colData(sce)$FSC_A
fsc_h <- colData(sce)$FSC_H

# Model expected singlet relationship
fit <- lm(fsc_a ~ fsc_h)
residuals <- abs(fsc_a - predict(fit))
threshold <- quantile(residuals, 0.95)

# Mark doublets
colData(sce)$doublet <- residuals > threshold
sce_singlets <- sce[, !colData(sce)$doublet]

cat('Doublet rate:', round(mean(colData(sce)$doublet) * 100, 1), '%\n')

Batch Processing

# Process all samples
detect_doublets <- function(ff, method = 'fsc') {
    if (method == 'fsc') {
        fsc_a <- exprs(ff)[, 'FSC-A']
        fsc_h <- exprs(ff)[, 'FSC-H']

        fit <- lm(fsc_a ~ fsc_h)
        residuals <- abs(fsc_a - predict(fit))
        threshold <- quantile(residuals, 0.95)

        singlet_idx <- residuals <= threshold
    } else if (method == 'ratio') {
        ratio <- exprs(ff)[, 'FSC-A'] / (exprs(ff)[, 'FSC-H'] + 1)
        singlet_idx <- ratio < quantile(ratio, 0.95)
    }

    return(ff[singlet_idx, ])
}

# Apply to all samples
fs_singlets <- fsApply(fs, detect_doublets, method = 'fsc')

# Report
doublet_rates <- sapply(1:length(fs), function(i) {
    1 - nrow(fs_singlets[[i]]) / nrow(fs[[i]])
})
cat('Mean doublet rate:', round(mean(doublet_rates) * 100, 1), '%\n')

Visualization

library(ggplot2)

# Extract data for plotting
plot_data <- data.frame(
    FSC_A = exprs(fs[[1]])[, 'FSC-A'],
    FSC_H = exprs(fs[[1]])[, 'FSC-H']
)

# Calculate doublet status
fit <- lm(FSC_A ~ FSC_H, data = plot_data)
plot_data$residual <- abs(plot_data$FSC_A - predict(fit))
plot_data$doublet <- plot_data$residual > quantile(plot_data$residual, 0.95)

# Plot
ggplot(plot_data, aes(x = FSC_H, y = FSC_A, color = doublet)) +
    geom_point(alpha = 0.3, size = 0.5) +
    scale_color_manual(values = c('gray', 'red')) +
    theme_bw() +
    labs(title = 'Doublet Detection', x = 'FSC-H', y = 'FSC-A')
ggsave('doublet_detection.png', width = 8, height = 6)

Related Skills

Workflow order: cytometry-qc → doublet-detection → bead-normalization → clustering

• cytometry-qc - Run first: identify flow rate and signal issues
• bead-normalization - Run after: correct remaining instrument drift
• fcs-handling - Load FCS files
• gating-analysis - Manual gating workflows
• clustering-phenotyping - Downstream analysis after doublet removal