Bio Flow Cytometry Compensation Transformation

Version Compatibility

Reference examples tested with: flowCore 2.14+, scanpy 1.10+

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.

Compensation and Transformation

"Compensate and transform my flow cytometry data" → Correct spectral overlap between fluorophores using a compensation matrix and apply biexponential/arcsinh transforms for visualization and analysis.

• R: flowCore::compensate() then flowCore::transform() with estimateLogicle()

Load Compensation Matrix

library(flowCore)

# From FCS file keywords
fcs <- read.FCS('sample.fcs', transformation = FALSE)
comp_matrix <- keyword(fcs)$`$SPILLOVER`

# Or from CSV file
comp_matrix <- as.matrix(read.csv('compensation.csv', row.names = 1))

Apply Compensation

# Create compensation object
comp <- compensation(comp_matrix)

# Apply to flowFrame
fcs_comp <- compensate(fcs, comp)

# Apply to flowSet
fs_comp <- compensate(fs, comp)

Calculate Compensation from Controls

library(flowStats)

# Single-stained controls
controls <- read.flowSet(list.files('controls', pattern = '\\.fcs$', full.names = TRUE))

# Calculate spillover matrix
spillover <- spillover(controls,
                        unstained = 'Unstained.fcs',
                        fsc = 'FSC-A', ssc = 'SSC-A',
                        patt = '-A$',  # Channel pattern
                        stain_match = 'regexpr')

# The result is a list; extract matrix
comp_matrix <- spillover$comp

Transformation: Biexponential (Logicle)

# Logicle transformation (standard for flow)
library(flowWorkspace)

# Auto-estimate parameters
lgcl <- estimateLogicle(fcs, colnames(fcs)[3:10])

# Apply
fcs_trans <- transform(fcs, lgcl)

# Manual logicle parameters
lgcl_manual <- logicleTransform(
    w = 0.5,      # Linearization width
    t = 262144,   # Top of scale
    m = 4.5,      # Decades of data
    a = 0         # Additional negative range
)

Transformation: Arcsinh (CyTOF)

# Arcsinh transformation for CyTOF
arcsinh_transform <- function(x, cofactor = 5) {
    asinh(x / cofactor)
}

# Apply to expression matrix
expr <- exprs(fcs)
expr_trans <- apply(expr[, marker_channels], 2, arcsinh_transform, cofactor = 5)

# Or using transformList
asinhTrans <- arcsinhTransform(transformationId = 'arcsinh', a = 0, b = 1/5)
trans_list <- transformList(marker_channels, asinhTrans)
fcs_trans <- transform(fcs, trans_list)

Transformation: Log

# Simple log transformation
logTrans <- logTransform(transformationId = 'log10', logbase = 10, r = 1, d = 1)
trans_list <- transformList(marker_channels, logTrans)
fcs_trans <- transform(fcs, trans_list)

View Before/After Compensation

library(ggcyto)

# Before compensation
p1 <- autoplot(fcs, 'FITC-A', 'PE-A') + ggtitle('Before Compensation')

# After compensation
p2 <- autoplot(fcs_comp, 'FITC-A', 'PE-A') + ggtitle('After Compensation')

library(patchwork)
p1 + p2

Complete Preprocessing Pipeline

Goal: Apply a standard compensation-then-transformation workflow to all samples in a flowSet.

Approach: Define a reusable preprocessing function that first applies the spillover compensation matrix, then auto-estimates and applies logicle transformation on marker channels, and map it across all samples with fsApply.

preprocess_flow <- function(fcs, comp_matrix, marker_channels) {
    # 1. Compensation
    comp <- compensation(comp_matrix)
    fcs <- compensate(fcs, comp)

    # 2. Transformation (logicle for flow, arcsinh for CyTOF)
    lgcl <- estimateLogicle(fcs, marker_channels)
    fcs <- transform(fcs, lgcl)

    return(fcs)
}

# Apply to flowSet
fs_processed <- fsApply(fs, function(f) {
    preprocess_flow(f, comp_matrix, marker_channels)
})

CATALYST Preprocessing (CyTOF)

library(CATALYST)
library(SingleCellExperiment)

# Create SingleCellExperiment from flowSet
sce <- prepData(fs,
                panel = panel,      # data.frame with columns: fcs_colname, antigen, marker_class
                md = sample_info,   # sample metadata
                transform = TRUE,   # Apply arcsinh
                cofactor = 5,
                FACS = FALSE)       # TRUE for flow, FALSE for CyTOF

Panel File Format (CATALYST)

# panel.csv
panel <- data.frame(
    fcs_colname = c('Yb176Di', 'Er168Di', 'Nd142Di'),
    antigen = c('CD45', 'CD3', 'CD4'),
    marker_class = c('type', 'type', 'type')  # 'type' for phenotyping, 'state' for functional
)

Save Preprocessed Data

# Write transformed FCS
write.FCS(fcs_trans, 'sample_preprocessed.fcs')

# Save transformation for reproducibility
saveRDS(list(comp = comp_matrix, transform = lgcl), 'preprocessing_params.rds')

Related Skills

• fcs-handling - Load FCS files first
• gating-analysis - Gate after preprocessing
• clustering-phenotyping - Cluster transformed data

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