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bio-flow-cytometry-gating-analysis

Maintainer FreedomIntelligence · Last updated April 1, 2026

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.

OpenClawNanoClawAnalysisReproductionbio-flow-cytometry-gating-analysis🧬 bioinformatics (gptomics bio-* suite)bioinformatics — immunoinformatics & flow cytometrymanual

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-flow-cytometry-gating-analysis

Maintainer: FreedomIntelligence
License: MIT
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

R: flowWorkspace::gs_add_gating_method(), openCyto::gating() for automated gating.
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.
cd4_gate <- rectangleGate(filterId = 'CD4+', 'CD4' = c(500, Inf), 'CD3' = c(200, Inf)).

Source Doc

Excerpt From SKILL.md

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')