ReddRadar
Agent skill
bio-flow-cytometry-gating-analysis
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.
Install this agent skill to your Project
npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-flow-cytometry-gating-analysis
SKILL.md
Version Compatibility
Reference examples tested with: flowCore 2.14+
Before using code patterns, verify installed versions match. If versions differ:
- R:
packageVersion('<pkg>')then?function_nameto verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Gating Analysis
"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.
- R:
flowWorkspace::gs_add_gating_method(),openCyto::gating()for automated gating
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
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)
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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