bio-flow-cytometry-gating-analysis

name	bio-flow-cytometry-gating-analysis
description	Defines cell populations in flow and spectral cytometry through manual gates (rectangle, polygon, quadrant, boolean) and reproducible automated gating (openCyto gating templates, flowDensity data-driven thresholds, flowClust model-based gates), organized as a hierarchical GatingSet (flowWorkspace) and round-tripped with FlowJo via CytoML. Covers the canonical gate order (time -> debris -> singlets -> live -> lineage), FMO-vs-isotype boundary setting, gate-order dependence and recompute semantics, rare-event/MRD gating, and per-population statistics. Use when building a gating strategy, automating a manual FlowJo scheme across samples, choosing manual vs data-driven gates, or extracting population frequencies.
tool_type	r
primary_tool	flowWorkspace

Version Compatibility

Reference examples tested with: flowWorkspace 4.14+, openCyto 2.14+, flowDensity 1.36+, flowCore 2.14+, CytoML 2.14+.

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

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

openCyto gating-method names drift across versions - confirm with gt_list_methods() on the installed package (e.g. gate_flowclust_2d vs flowClust.2d). Adapt rather than retrying.

Gating Analysis

"Gate my data to identify cell populations" -> Define populations by drawing boundaries in marker space, organized as a hierarchy, manually or with reproducible data-driven methods.

• R (manual + hierarchy): flowCore gates -> flowWorkspace::GatingSet -> gs_pop_add -> recompute
• R (automated): openCyto gating template (CSV) or flowDensity::deGate

The Single Most Important Modern Insight -- FMO, Not Isotype, Sets the Boundary; and Gate Order Is a Funnel

The position of a positive/negative boundary is governed by SPREADING ERROR - the variance that every other bright fluorophore spills into the channel of interest - NOT by nonspecific antibody binding (Roederer 2001 Cytometry 45:194). An FMO control (full panel minus the one channel) reproduces exactly that spreading and is the correct way to set the gate; an isotype control addresses only nonspecific binding, has a different total fluorochrome load, and sits in the wrong place. Isotypes are deprecated for boundary-setting (still fine for a qualitative new-reagent check). Equally load-bearing is gate ORDER: time -> debris (FSC/SSC) -> singlets (FSC-A vs FSC-H) -> live/dead -> lineage. This is a funnel that removes the broadest, least-specific contaminants first (time instability corrupts ALL channels; doublets are scatter-normal AND viable AND double-positive; dead cells bind antibody nonspecifically) so each narrower downstream gate operates on clean input. Reorder it - gate lineage before singlets - and artifacts are baked into the result that no later gate can remove.

Automated-Gating Taxonomy

Method	Citation	Mechanism	When to use
openCyto	Finak 2014 PLoS Comput Biol 10:e1003806	CSV gatingTemplate + per-gate algorithms	reproduce a manual SOP across many samples; human-readable + automated
mindensity (openCyto)	-	KDE valley between two peaks	clear bimodal marker, 1D cut
tailgate (openCyto)	-	KDE-derivative tail onset	rare positive tail, no clean second peak
quantileGate (openCyto)	-	cut at a fixed event quantile	threshold should track a fraction
flowDensity	Malek 2015 Bioinformatics 31:606	sequential bivariate density cutoffs	reproduce an entire predefined manual strategy
flowClust / gate_flowclust_2d	Lo 2009 BMC Bioinformatics 10:145	t-mixture + Box-Cox, K by BIC	overlapping elliptical populations
DAFi	Lee 2018 Cytometry A 93:597	recursive filter + clustering on a hierarchy	discovery WITH interpretability

Rule of thumb: 1D bimodal -> mindensity; rare tail -> tailgate; overlapping ellipses -> flowClust.2d; replicate a full manual SOP -> flowDensity; discovery-with-interpretability -> DAFi.

Build a Gating Hierarchy

Goal: Apply gates in the canonical order and extract population statistics.

Approach: Build a GatingSet, add gates parent-by-parent, then recompute() - WITHOUT it, child populations are empty. Gates apply on the TRANSFORMED scale if the GatingSet is transformed.

library(flowWorkspace); library(flowCore)

gs <- GatingSet(fs)
# matrix dimnames preserve 'FSC-A'/'FSC-H'; data.frame() would mangle them to FSC.A
singlet <- polygonGate('singlets', .gate = matrix(
  c(2e4, 1e4, 25e4, 2e5, 25e4, 26e4, 2e4, 4e4), ncol = 2, byrow = TRUE,
  dimnames = list(NULL, c('FSC-A', 'FSC-H'))))
gs_pop_add(gs, singlet, parent  
gs_pop_addgs rectangleGate CD3    parent    
recomputegs                                   
gs_pop_get_statsgs type  

Automated Gating with an openCyto Template

Goal: Apply a reproducible, declarative gating strategy across all samples.

Approach: A CSV template (alias/pop/parent/dims/gating_method/gating_args) defines the hierarchy; gt_gating applies it. Confirm method names with gt_list_methods().

library(openCyto); library(data.table)

tmpl <- fread('
alias,pop,parent,dims,gating_method,gating_args
nonDebris,+,root,FSC-A,mindensity,
singlets,+,nonDebris,"FSC-A,FSC-H",singletGate,
live,-,singlets,"Live_Dead",mindensity,
CD3,+,live,CD3,mindensity,
CD4CD8,+,CD3,"CD4,CD8",gate_flowclust_2d,K=2
')
gt <- gatingTemplate(tmpl)
gs <- GatingSet(fs)
gt_gating(gt, gs)

Rare-Event / MRD Gating

Goal: Detect a rare population (e.g. MRD at 1e-4 to 1e-5).

Approach: Unsupervised clustering FAILS here (a 1e-5 population is ~10 events, invisible to density/SOM); MRD stays supervised/template-gated. Compute the acquisition depth needed from the target sensitivity and the ~50-event Poisson rule BEFORE acquiring; never downsample.

# Need ~50-60 target events for CV < ~15%; sensitivity 1e-5 => acquire ~1e6 cells.
target_sensitivity <- 1e-5
events_needed <- ceiling(50 / target_sensitivity)   # cells to acquire
# Gate the rare population with a prespecified template; report observed LOD from cells acquired.

Per-Method Failure Modes

Empty child populations

Trigger: querying stats right after gs_pop_add. Mechanism: membership not computed. Symptom: zero counts. Fix: recompute(gs).

Gate coordinates on the wrong scale

Trigger: raw-scale gate values on a transformed GatingSet (or vice versa). Mechanism: scale mismatch. Symptom: gate in the wrong place / empty. Fix: set gate values on the same (transformed) scale the GS uses.

Isotype-defined boundary

Trigger: isotype control to set positivity. Mechanism: spreading error, not nonspecific binding, sets the edge. Symptom: wrong negative boundary. Fix: use FMO.

Clustering used for rare events

Trigger: FlowSOM for a 1e-5 population. Mechanism: too few events. Symptom: rare pop absorbed into a neighbor. Fix: supervised/template gating; size acquisition for the Poisson floor.

Quantitative Thresholds

Threshold	Source	Rationale
~50-60 events for CV < 15%	Poisson statistics	rare-event detection floor
sensitivity 1e-5 needs ~1e6 cells	Poisson floor	to collect ~50 events at that frequency
FMO for boundary, not isotype	Roederer 2001; Maecker & Trotter 2006	spreading error dominates the boundary

Common Errors

Error / symptom	Cause	Solution
zero counts in children	no recompute()	call it after adding gates
gt_gating method not found	version-renamed method	check gt_list_methods()
filter() vs Subset() confusion	filter returns a mask, Subset the data	use Subset(ff, gate) for the population
FlowJo .jo won't import	only .wsp supported	re-save as wsp; use CytoML

References

• Roederer 2001 Cytometry 45(3):194-205 — spreading error sets the gate boundary.
• Maecker & Trotter 2006 Cytometry A 69(9):1037-1042 — FMO doctrine, controls, positivity.
• Finak 2014 PLoS Comput Biol 10(8):e1003806 — openCyto automated gating templates.
• Malek 2015 Bioinformatics 31(4):606-607 — flowDensity data-driven gating.
• Lo 2009 BMC Bioinformatics 10:145 — flowClust model-based gating.
• Lee 2018 Cytometry A 93(6):597-610 — DAFi directed filtering + clustering.
• Spidlen 2015 Cytometry A 87(7):683-687 — Gating-ML 2.0 portable gate standard.

Related Skills

• compensation-transformation - Preprocess before gating; gate on the transformed scale
• doublet-detection - The singlet step of the gating funnel
• clustering-phenotyping - Unsupervised alternative for high-dim discovery
• differential-analysis - Compare gated population frequencies between conditions
• fcs-handling - Load FCS and import FlowJo workspaces via CytoML