数据与复现生物信息与基因组学FreedomIntelligence/OpenClaw-Medical-Skills数据与复现

bio-flow-cytometry-gating-analysis

维护者 FreedomIntelligence · 最近更新 2026年4月1日

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.

OpenClawNanoClaw分析处理复现实验bio-flow-cytometry-gating-analysis🧬 bioinformatics (gptomics bio-* suite)bioinformatics — immunoinformatics & flow cytometrymanual

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

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

维护者: FreedomIntelligence
许可: MIT
最近更新: 2026年4月1日

技能摘要

来自 SKILL.md 的关键信息

2 min

核心说明

R：flowWorkspace：：gs_add_gating_method()，openCyto：：gating() ，用于 automated gating。
Gate my flow cytometry data to identify cell populations" → Define cell populations through manual 或 automated gating strategies ，使用 rectangular，polygon，或 data-driven gates in hierarchical 框架. R：flowWorkspace：：gs_add_gating_method()，openCyto：：gating() ，用于 automated gating。
cd4_gate <- rectangleGate(filterId = 'CD4+'，'CD4' = c(500，Inf)，'CD3' = c(200，Inf))。

原始文档

SKILL.md 摘录

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')