Identify cell populations in flow cytometry data using manual boundaries or automated methods. Organize populations into a hierarchy, extract matching cells, summarize counts, visualize boundaries, and save or export the analysis.
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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.
安装前可以查看交给 AI 代理的指令内容。
Reference examples tested with: flowCore 2.14+
Before using code patterns, verify installed versions match. If versions differ:
packageVersion('<pkg>') then ?function_name to verify parametersIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"Gate my flow cytometry data to identify cell populations" → Define cell populations through manual or automated gating strategies using rectangular, polygon, or data-driven gates in a hierarchical framework.
flowWorkspace::gs_add_gating_method(), openCyto::gating() for automated gatinglibrary(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)
# 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)
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)
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)
Goal: Apply a reproducible, template-driven gating strategy that automatically identifies cell populations across all samples.
Approach: Define a CSV gating template specifying parent-child hierarchy, channel combinations, and gating algorithms (flowClust, singletGate, mindensity, quadrantGate), then apply the template to a GatingSet for batch processing.
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)
# 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-
# 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')
# 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)
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)
# Save GatingSet
save_gs(gs, 'gating_set')
# Export to FlowJo workspace
library(CytoML)
gatingset_to_flowjo(gs, 'analysis.wsp')
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