Bio Flow Cytometry Fcs Handling

Version Compatibility

Reference examples tested with: flowCore 2.14+, flowWorkspace 4.14+, CytoML 2.14+; Python flowkit 1.1+, readfcs 1.1+.

Before using code patterns, verify installed versions match. If versions differ:

• R: packageVersion('<pkg>') then ?function_name to verify parameters
• Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

FCS File Handling

"Load my FCS files and inspect the channels" -> Parse FCS format into event matrix + parameter metadata, map detector channels to antibodies, and choose a reader appropriate to the instrument and downstream ecosystem.

• R: flowCore::read.FCS() / read.flowSet() -> flowFrame/flowSet; CytoML::flowjo_to_gatingset() for FlowJo workspaces
• Python: flowkit.Sample() (full workflow) or readfcs.read() -> AnnData (scanpy/scverse bridge)

The Single Most Important Modern Insight -- read.FCS Silently Transforms by Default

flowCore::read.FCS() defaults to transformation = "linearize", which APPLIES the $PnE log-amplification scaling on read. Two pipelines reading "the same raw FCS" (flowCore default vs fcsparser/transformation=FALSE) therefore return different numbers, and a compensation matrix computed on one will silently mismatch the other. For any preprocessing pipeline that will compensate and transform downstream, read with transformation = FALSE (or NULL) to get the genuinely raw values, and set truncate_max_range = FALSE so out-of-$PnR events (common on CyTOF and some digital instruments) are not silently clipped. Decide the read settings deliberately; they are not nuisance defaults.

FCS Standard Internals (what the keywords mean)

FCS standards: 3.0 (Seamer 1997 Cytometry 28:118), 3.1 (Spidlen 2010 Cytometry A 77:97), 3.2 (Spidlen 2021 Cytometry A 99:100). Area/Height/Width = pulse integral/peak/duration; FSC-A vs FSC-H is the doublet axis. CyTOF channels are <Metal><Mass>Di (e.g. Yb176Di) and report dual counts (pulse-counting at low signal, intensity at high).

Reader Taxonomy

Load and Inspect FCS (R)

Goal: Read one file (or a directory) raw, inspect parameters, and map channels to antibodies.

Approach: Read with transformation=FALSE, truncate_max_range=FALSE; the channel->antibody map lives in pData(parameters(fcs)) (name = detector, desc = antibody).

library(flowCore)

fcs <- read.FCS('sample.fcs', transformation = FALSE, truncate_max_range = FALSE)
params <- pData(parameters(fcs))          # name (detector), desc (antibody), range, minRange
channel_map <- setNames(params$desc, params$name)

fs <- read.flowSet(list.files('data', pattern = '\\.fcs$', full.names = TRUE),
                   transformation = FALSE, truncate_max_range = FALSE)
expr <- exprs(fcs)                         # cells x channels

Access the Compensation Matrix from Keywords

Goal: Retrieve the acquisition-recorded spillover matrix, handling the three keyword conventions.

Approach: Try $SPILLOVER, then the legacy SPILL, then $COMP; flowCore::spillover() resolves the standard slots.

kw <- keyword(fcs)
spill <- kw$`$SPILLOVER`
if (is.null(spill)) spill <- kw$SPILL          # digital BD convention
if (is.null(spill)) spill <- kw$`$COMP`        # legacy FCS 3.0 (unnamed columns)

Load FCS in Python (FlowKit / readfcs)

Goal: Read FCS in a Python pipeline, either for FlowKit's compensation/gating or as an AnnData for scanpy.

Approach: flowkit.Sample exposes raw/compensated/transformed events as DataFrames; readfcs.read returns AnnData with channels in var.

import flowkit as fk
import readfcs

sample = fk.Sample('sample.fcs')
events = sample.as_dataframe(source='raw')   # source in {'raw','comp','xform'}

adata = readfcs.read('sample.fcs')           # AnnData; adata.var has channel + antibody names

Rename Channels, Subset, Write, Annotate Samples

Goal: Standardize channel names to antibodies and attach sample-level metadata for downstream tools.

Approach: Replace blank desc with name; attach a pData table keyed by sampleNames(fs) (CATALYST/diffcyt require this).

new <- ifelse(is.na(params$desc) | params$desc == '', params$name, params$desc)
colnames(fcs) <- new

fcs_markers <- fcs[, c('CD4', 'CD8', 'CD3')]          # subset channels
write.FCS(fcs, 'out.fcs')

pData(fs) <- data.frame(name = sampleNames(fs),
                        condition = c('Control','Control','Treatment','Treatment'),
                        patient = c('P1','P2','P1','P2'),
                        row.names = sampleNames(fs))

Per-Method Failure Modes

Silent log-linearization on read

Trigger: read.FCS('x.fcs') with default args. Mechanism: transformation="linearize" applies $PnE scaling. Symptom: values differ from fcsparser; compensation matrix mismatch. Fix: transformation = FALSE.

Out-of-range clipping

Trigger: instrument wrote values above $PnR (common CyTOF). Mechanism: truncate_max_range=TRUE (default) clamps them. Symptom: a ceiling artifact at the channel max. Fix: truncate_max_range = FALSE.

Channel names break formulas

Trigger: channels like FSC-A, Pacific Blue-A. Mechanism: hyphens/spaces are not syntactic R names. Symptom: formula/gating errors. Fix: alter.names = TRUE on read.

FlowJo parsing in the wrong package

Trigger: looking for FlowJo import in flowWorkspace. Mechanism: parsing lives in CytoML. Symptom: function-not-found. Fix: CytoML::open_flowjo_xml() -> flowjo_to_gatingset(); only .wsp (FlowJo 10+), not legacy .jo.

Common Errors

References

• Seamer 1997 Cytometry 28(2):118-122 — FCS 3.0 standard.
• Spidlen 2010 Cytometry A 77(1):97-100 — FCS 3.1 standard.
• Spidlen 2021 Cytometry A 99(1):100-102 — FCS 3.2 standard.
• Finak 2018 Cytometry A 93(12):1189-1196 — CytoML cross-platform gating import/export.
• White 2021 Front Immunol 12:768541 — FlowKit Python toolkit.
• Lee 2008 Cytometry A 73(10):926-930 — MIFlowCyt minimum reporting standard.

Related Skills

• compensation-transformation - Compensate and transform after loading
• cytometry-qc - Assess acquisition quality on the loaded data
• gating-analysis - Define populations from the loaded GatingSet
• clustering-phenotyping - Unsupervised analysis of the event matrix
• single-cell/data-io - readfcs bridges FCS to the AnnData/scanpy ecosystem
• imaging-mass-cytometry/data-preprocessing - Shared metal-channel and FCS conventions