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.
Best use case
bio-flow-cytometry-gating-analysis is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using bio-flow-cytometry-gating-analysis should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/bio-flow-cytometry-gating-analysis/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How bio-flow-cytometry-gating-analysis Compares
| Feature / Agent | bio-flow-cytometry-gating-analysis | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
## 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_name` to 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
```r
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
```r
# 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)
```r
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
```r
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.
```r
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
```r
# 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
```r
# 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
```r
# 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
```r
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
```r
# 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 populationsRelated Skills
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