Gating Analysis
Manual Rectangular Gates
library(flowCore)
# Create rectangular gate
cd4_gate <- rectangleGate(filterId = 'CD4+',
'CD4' = c(500, Inf),
'CD3' = c(200, Inf))
# Apply gate
cd4_result <- filter(fcs, cd4_gate)
summary(cd4_result)
# Get cells in gate
cd4_cells <- Subset(fcs, cd4_gate)
Polygon Gates
# Define polygon vertices
vertices <- matrix(c(100, 100, # x1, y1
1000, 100, # x2, y2
1000, 1000, # x3, y3
100, 1000), # x4, y4
ncol = 2, byrow = TRUE)
colnames(vertices) <- c('FSC-A', 'SSC-A')
# Create polygon gate
poly_gate <- polygonGate(filterId = 'Lymphocytes', .gate = vertices)
# Apply
lymph <- Subset(fcs, poly_gate)
Gating Hierarchy (flowWorkspace)
library(flowWorkspace)
# Create GatingSet from flowSet
gs <- GatingSet(fs)
# Add gates to hierarchy
gs_pop_add(gs, cd4_gate, parent = 'root')
# Add child gate
cd4_cd8_gate <- rectangleGate(filterId = 'CD8+', 'CD8' = c(500, Inf))
gs_pop_add(gs, cd4_cd8_gate, parent = 'CD4+')
# View hierarchy
gs_get_pop_paths(gs)
# Recompute statistics
recompute(gs)
# Get population statistics
gs_pop_get_stats(gs)
Automated Gating: flowDensity
library(flowDensity)
# Data-driven gate based on density
cd4_gate <- deGate(fcs, channel = 'CD4', use.upper = TRUE)
# Get threshold
cd4_threshold <- cd4_gate@min
# Apply
cd4_pos <- flowDensity(fcs, channels = 'CD4', position = c(TRUE))
cd4_cells <- getflowFrame(cd4_pos)
Automated Gating: openCyto
library(openCyto)
# Define gating template
gating_template <- fread('
alias,pop,parent,dims,gating_method,gating_args
nonDebris,+,root,FSC-A,flowClust,K=2
singlets,+,nonDebris,"FSC-A,FSC-H",singletGate,
lymph,+,singlets,"FSC-A,SSC-A",flowClust,K=3
cd3,+,lymph,CD3,mindensity,
cd4,+,cd3,"CD4,CD8",quadrantGate,
')
# Apply template
gt <- gatingTemplate(gating_template)
gs <- GatingSet(fs)
gating(gt, gs)
Quadrant Gates
# Create quadrant gate
quad_gate <- quadGate(filterId = 'CD4_CD8_quad',
'CD4' = 500,
'CD8' = 500)
# Results in 4 populations:
# CD4+CD8-, CD4-CD8+, CD4+CD8+, CD4-CD8-
Boolean Gates
# Combine gates with logic
cd4_not_cd8 <- cd4_gate & !cd8_gate
# Alternative using GatingSet
gs_pop_add(gs,
booleanFilter(CD4+CD8- = CD4+ & !CD8+),
parent = 'lymph')
Extract Gated Populations
# Get data for specific population
cd4_data <- gh_pop_get_data(gs[[1]], 'CD4+')
# Get indices
cd4_indices <- gh_pop_get_indices(gs[[1]], 'CD4+')
# Counts
gs_pop_get_count_fast(gs)
Visualization
library(ggcyto)
# Plot with gates
autoplot(gs[[1]], 'CD4+')
# Multiple populations
autoplot(gs[[1]], c('CD4+', 'CD8+'))
# Gate overlay
autoplot(fcs, 'CD4', 'CD8') +
geom_gate(cd4_gate)
Export Gating Strategy
# Save GatingSet
save_gs(gs, 'gating_set')
# Export to FlowJo workspace
library(CytoML)
gatingset_to_flowjo(gs, 'analysis.wsp')
Related Skills
- compensation-transformation - Preprocess before gating
- clustering-phenotyping - Unsupervised alternative
- differential-analysis - Compare gated populations
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