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-doublet-detection

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

Detect and remove doublets from flow and mass cytometry data. Covers FSC/SSC gating and computational doublet detection methods. Use when filtering out cell aggregates before clustering or quantitative analysis.

OpenClawNanoClaw分析处理复现实验bio-flow-cytometry-doublet-detection🧬 bioinformatics (gptomics bio-* suite)bioinformatics — immunoinformatics & flow cytometrydetect

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

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

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：flowCore rectangular gates on FSC- vs FSC-H。
Remove doublets ，面向 my flow cytometry data" → Detect 、 filter out cell aggregates ，使用 FSC- /FSC-H gating 或 computational methods before 聚类或 quantitative analysis. R：flowCore rectangular gates on FSC- vs FSC-H。
fs <- read.flowSet(list.files('data/'，pattern = '\\.fcs$'，full.names = TRUE))。

原始文档

SKILL.md 摘录

FSC-A vs FSC-H Gating (Standard Method)

library(flowCore)
library(ggcyto)

## Manual rectangular gate

singlet_gate <- rectangleGate(
    filterId = 'singlets',
    'FSC-A' = c(50000, 250000),
    'FSC-H' = c(50000, 250000)
)

## Or use polygon gate for diagonal

singlet_polygon <- polygonGate(
    filterId = 'singlets',
    .gate = data.frame(
        'FSC-A' = c(50000, 250000, 250000, 50000),
        'FSC-H' = c(40000, 200000, 260000, 60000)
    )
)