bio-flow-cytometry-cytometry-qc

name: bio-flow-cytometry-cytometry-qc description: Quality control for flow, spectral, and mass cytometry - time-based anomaly cleaning (flowAI, flowCut, PeacoQC, flowClean), margin/boundary event removal, signal-drift detection, dead-cell exclusion, CyTOF Gaussian/DNA/event-length checks, instrument calibration/standardization (MESF, CS&T, peak-2), and batch-level outlier flagging. Use when assessing acquisition quality, choosing a cleaning tool, ordering QC relative to compensation, deciding margin removal before density-based steps, or flagging problematic samples before clustering or differential analysis. tool_type: r primary_tool: flowAI

Version Compatibility

Reference examples tested with: flowAI 1.32+, PeacoQC 1.12+, flowCore 2.14+, flowDensity 1.36+, CATALYST 1.26+.

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

• R: packageVersion('<pkg>') then ?function_name to verify parameters

Counterintuitive defaults to confirm: flowAI checks are FR/FS/FM (FM = dynamic range, not "flow"); PeacoQC MAD/IT_limit are LESS strict when HIGHER. Verify with ?flow_auto_qc and ?PeacoQC before tuning.

Cytometry QC

"Run quality control on my cytometry data" -> Detect and remove acquisition artifacts (flow-rate instability, signal drift, margin events, dead cells, CyTOF doublets) on the Time axis, then flag outlier samples.

• R (flow/spectral): flowAI::flow_auto_qc(), PeacoQC::PeacoQC() (+ RemoveMargins())
• R (mass): CATALYST::normCytof() beads + Gaussian/DNA/event-length gating

The Single Most Important Modern Insight -- The Time Parameter Is the Master QC Axis, and Order Matters

Nearly every acquisition artifact - clogs, bubbles, flow-rate surges, electronics warm-up, CyTOF sensitivity decay, oxide buildup - manifests as a CHANGE IN SIGNAL versus the Time channel. flowAI, flowCut, flowClean, and PeacoQC are all, at heart, Time-vs-signal anomaly detectors; a missing or mis-scaled $TIMESTEP silently degrades or breaks all of them. Just as important is the ORDER: compensation/unmixing -> transform -> margin removal -> time-based QC -> debris/doublet/dead-cell gating -> batch normalization. Margin (boundary) events piled at a detector min/max form spurious high-density ridges that fool density-based cleaning and density gates, so they must be stripped BEFORE any density step; and time-based QC on untransformed data misbehaves because the density structure the algorithms rely on lives on the transformed scale.

Cleaning-Tool Taxonomy

Run flowAI (with the correct API)

Goal: Auto-clean a sample for flow-rate, signal-acquisition, and dynamic-range anomalies.

Approach: flow_auto_qc() returns a flowFrame of high-quality events when output=1; FM is the dynamic-range check; supply timeCh for concatenated/clock-reset files. flowAI is the time-based QC step - run it after compensation/transform/margin removal (per the ordering above), not on a raw uncompensated frame.

library(flowAI)

ff_clean <- flow_auto_qc(ff,
                         remove_from = 'all',          # FR + FS + FM
                         output = 1,                    # 1 = HQ events only; 2 = add QC param; 3 = bad-event IDs
                         ChExcludeFS = c('FSC', 'SSC'), # scatter excluded from the signal check
                         second_fractionFR = 0.1,
                         folder_results = 'qc_output')
cat('kept', nrow(ff_clean), 'of', nrow(ff), 'events\n')

Margins First, Then PeacoQC

Goal: Remove boundary events, then clean unstable time/peak structure across all channels.

Approach: RemoveMargins() strips detector-min/max events; then PeacoQC() - remember higher MAD/IT_limit = LESS strict.

library(PeacoQC)

ff_nm <- RemoveMargins(ff, channels = c('FSC-A', 'SSC-A'))   # do this BEFORE density QC
res <- PeacoQC(ff_nm, channels = marker_channels,
               MAD = 6, IT_limit = 0.55,                     # defaults; higher = less strict (counterintuitive)
               save_fcs = FALSE, plot = TRUE)
ff_clean <- res$FinalFF

Dead-Cell, Drift, and CyTOF Checks

Goal: Exclude dead cells, detect per-channel drift, and apply CyTOF-specific gates.

Approach: Viability dye threshold (bimodal); per-time-bin median slope for drift; for CyTOF use DNA intercalator + Gaussian/event-length; EQ-bead-median-vs-Time is the primary CyTOF drift readout (see bead-normalization).

expr <- exprs(ff)
# dead cells take up more viability dye -> cut at the bimodal density VALLEY (data-driven), not a fixed quantile
dead_cut <- flowDensity::deGate(ff, channel = 'Live_Dead')
live <- expr[, 'Live_Dead'] < dead_cut

# CyTOF single-cell gates
if ('Event_length' %in% colnames(expr)) {
    keep <- expr[, 'Event_length'] >= 10 & expr[, 'Event_length'] <= 75   # confirm range per instrument
}
dna <- grep('Ir191|Ir193', colnames(expr), value = TRUE)             # intercalator-positive = nucleated

Calibration and Standardization (cross-study comparability)

A discovery analyst often skips this, but cross-experiment/cross-site MFI comparison is meaningless without it (Maecker & Trotter 2006 Cytometry A 69:1037):

• MESF / MEF / ERF beads express intensity in molecules-of-equivalent-fluorochrome - comparable across instruments and time (NIST/ISAC standard; PE/Pacific Blue use ERF surrogates).
• Quantibrite PE (defined PE molecules/bead, ~1:1 conjugation) converts MFI to antibodies-bound-per-cell / receptor density (bead values are LOT-dependent).
• CS&T / 8-peak rainbow beads for daily QC (laser delay, area scaling, linearity).
• Peak-2 / voltration: run a dim particle across PMT voltages, pick the CV-vs-voltage inflection = minimum voltage for optimal resolution. This is why MIFlowCyt mandates reporting voltages.

Batch-Level Outlier Flagging

Goal: Flag samples whose event count, flow stability, or marker medians deviate from the batch.

Approach: Per-file metrics + MAD-based bounds; track an anchor/reference sample if present.

qc <- do.call(rbind, lapply(fcs_files, function(f) {
  ff <- read.FCS(f); e <- exprs(ff)
  data.frame(file = basename(f), events = nrow(ff),
             med_signal = median(apply(e, 2, median)))
}))
qc$outlier <- abs(qc$events - median(qc$events)) > 3 * mad(qc$events)

Per-Method Failure Modes

Density QC on un-margin-removed data

Trigger: PeacoQC/flowClean before RemoveMargins. Mechanism: axis pile-ups are false high-density ridges. Symptom: real events removed near the boundary, or margins kept. Fix: remove margins first.

flowAI over-removal

Trigger: default flowAI on a low-rate or short acquisition. Mechanism: FR check flags normal slow segments. Symptom: large unexplained event loss. Fix: raise second_fractionFR; inspect the HTML report; consider flowCut/PeacoQC.

QC on untransformed/uncompensated data

Trigger: running QC on raw linear values. Mechanism: high-intensity tail dominates density. Symptom: misplaced anomaly calls. Fix: compensate + transform first.

Time axis missing/reset

Trigger: concatenated files, some sorters. Mechanism: no usable Time. Symptom: flow-rate check fails or is meaningless. Fix: timeCh= or reconstruct; otherwise skip time-based checks.

Quantitative Thresholds

Common Errors

References

• Monaco 2016 Bioinformatics 32(16):2473-2480 — flowAI.
• Emmaneel 2022 Cytometry A 101(4):325-338 — PeacoQC.
• Meskas 2023 Cytometry A 103(1):71-81 — flowCut.
• Fletez-Brant 2016 Cytometry A 89(5):461-471 — flowClean.
• Fienberg 2012 Cytometry A 81(6):467-475 — cisplatin viability reagent (CyTOF live/dead).
• Maecker & Trotter 2006 Cytometry A 69(9):1037-1042 — controls, instrument setup, peak-2.
• Lee 2008 Cytometry A 73(10):926-930 — MIFlowCyt reporting (voltages, clones, config).

Related Skills

• compensation-transformation - Compensate/transform before time-based QC
• doublet-detection - Singlet discrimination after QC
• bead-normalization - EQ-bead drift correction for CyTOF (QC's normalization arm)
• clustering-phenotyping - Cluster only QC-passed events
• experimental-design/batch-design - Anchor/reference-sample design for batch QC