Bio Flow Cytometry Fcs Handling

Approach: Use flowCore's read.FCS with transformation disabled to load raw data, then examine parameter names and descriptions.

library(flowCore)

# Read single FCS file
fcs <- read.FCS('sample.fcs', transformation = FALSE, truncate_max_range = FALSE)

# File info
print(fcs)

# Parameter names
colnames(fcs)  # Short names
pData(parameters(fcs))  # Full metadata including descriptions

Load Multiple Files

Goal: Read a batch of FCS files into a single flowSet container for uniform processing.

Approach: List FCS files from a directory and load them into a flowSet with read.flowSet.

# Read multiple files into flowSet
files <- list.files('data', pattern = '\\.fcs$', full.names = TRUE)
fs <- read.flowSet(files, transformation = FALSE, truncate_max_range = FALSE)

# Sample names
sampleNames(fs)

# Access individual samples
fcs1 <- fs[[1]]

Access Expression Data

Goal: Extract the expression matrix from a flowFrame for numeric analysis.

Approach: Call exprs() to get the cells-by-channels matrix, then subset or summarize as needed.

# Get expression matrix
expr <- exprs(fcs)
head(expr)

# Dimensions
dim(expr)  # cells x channels

# Channel statistics
summary(expr)

# Get specific channels
cd4_expr <- expr[, 'CD4']

Channel Metadata

Goal: Retrieve channel names, descriptions, and ranges from the FCS parameter table.

Approach: Access the parameters slot via pData(parameters(fcs)) and build a short-name to description mapping.

# Parameter information
params <- pData(parameters(fcs))
print(params)

# Parameter columns:
# - name: short name (e.g., "FL1-A")
# - desc: description (e.g., "CD4")
# - range: max value
# - minRange: min value

# Get channel mapping
channel_map <- setNames(params$desc, params$name)

Rename Channels

# Rename using descriptions
rename_channels <- function(fcs) {
    params <- pData(parameters(fcs))
    new_names <- ifelse(is.na(params$desc) | params$desc == '',
                        params$name, params$desc)
    colnames(fcs) <- new_names
    return(fcs)
}

fcs_renamed <- rename_channels(fcs)

Subsetting Data

# Subset by cells (rows)
fcs_subset <- fcs[1:1000, ]

# Subset by channels (columns)
fcs_markers <- fcs[, c('CD4', 'CD8', 'CD3')]

# Subset by expression values
high_cd4 <- fcs[exprs(fcs)[, 'CD4'] > 1000, ]

Merge flowSets

# Combine multiple flowSets
fs_combined <- rbind2(fs1, fs2)

# Or concatenate into single flowFrame
all_data <- fsApply(fs, exprs)
all_data <- do.call(rbind, all_data)

Write FCS Files

# Write single file
write.FCS(fcs, 'output.fcs')

# Write flowSet
write.flowSet(fs, outdir = 'output_dir')

Sample Metadata

# Add sample annotations
pData(fs) <- data.frame(
    name = sampleNames(fs),
    condition = c('Control', 'Control', 'Treatment', 'Treatment'),
    patient = c('P1', 'P2', 'P1', 'P2')
)

# Access
pData(fs)

Basic Visualization

library(ggcyto)

# Density plot
autoplot(fcs, 'FSC-A')

# Bivariate plot
autoplot(fcs, 'CD4', 'CD8')

# Multiple samples
autoplot(fs, 'CD4', 'CD8')

Check Data Quality

# Time parameter check
if ('Time' %in% colnames(fcs)) {
    time <- exprs(fcs)[, 'Time']
    plot(time, type = 'l', main = 'Acquisition Time')
}

# Event count per file
fsApply(fs, nrow)

# Check for saturated events
saturation <- apply(exprs(fcs), 2, function(x) mean(x == max(x)) * 100)
print(saturation)

Convert to Data Frame

# For use with tidyverse
library(tidyverse)

df <- as.data.frame(exprs(fcs))
df$sample <- 'sample1'

# From flowSet
df_all <- fsApply(fs, function(f) {
    d <- as.data.frame(exprs(f))
    d$sample <- identifier(f)
    d
}, simplify = FALSE)
df_all <- bind_rows(df_all)

Related Skills

• compensation-transformation - Apply compensation and transforms
• gating-analysis - Define cell populations
• clustering-phenotyping - Unsupervised analysis