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npx versuz@latest install freedomintelligence-openclaw-medical-skills-skills-bio-flow-cytometry-compensation-transformationgit clone https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills.gitcp OpenClaw-Medical-Skills/SKILL.MD ~/.claude/skills/freedomintelligence-openclaw-medical-skills-skills-bio-flow-cytometry-compensation-transformation/SKILL.md---
name: bio-flow-cytometry-compensation-transformation
description: Spillover compensation and data transformation for flow cytometry. Covers compensation matrix calculation, application, and biexponential/arcsinh transforms. Use when correcting spectral overlap between fluorophores or transforming data for analysis.
tool_type: r
primary_tool: flowCore
---
## 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
```r
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
```r
# 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
```r
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)
```r
# 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)
```r
# 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
```r
# 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
```r
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.
```r
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)
```r
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)
```r
# 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
```r
# 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