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name: bio-flow-cytometry-gating-analysis description: Manual and automated gating for defining cell populations in flow cytometry. Covers rectangular, polygon, and data-driven gates. Use when identifying cell populations through hierarchical gating strategies. tool_type: r primary_tool: flowWorkspace measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

• read_file
• run_shell_command

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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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