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

bio-differential-expression-batch-correction

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

Remove batch effects from RNA-seq data using ComBat, ComBat-Seq, limma removeBatchEffect, and SVA for unknown batch variables. Use when correcting batch effects in expression data.

OpenClawNanoClaw分析处理复现实验bio-differential-expression-batch-correction🧬 bioinformatics (gptomics bio-* suite)bioinformatics — differential expression & transcriptomicsremove

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-differential-expression-batch-correction

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

  • corrected_counts <- ComBat_seq(counts = as.matrix(counts),batch = batch,group = condition,full_mod = TRUE)。
  • mod <- model.matrix(~ condition,data = metadata) mod0 <- model.matrix(~ 1,data = metadata)。

原始文档

SKILL.md 摘录

ComBat-Seq (Count Data)

Goal: Remove batch effects from raw count data while preserving biological group differences.

Approach: Apply ComBat-Seq's negative binomial regression to adjust counts, keeping the integer nature of the data.

"Remove batch effects from my RNA-seq counts" → Adjust raw count matrix for known batch labels using negative binomial modeling, preserving biological condition effects.

library(sva)

## ComBat (Normalized Data)

**Goal:** Remove batch effects from normalized (log-transformed or TPM) expression data.

**Approach:** Apply parametric empirical Bayes adjustment to normalized expression while protecting biological covariates.

```r
library(sva)

## Run ComBat

corrected_expr <- ComBat(dat = as.matrix(normalized_expr),
                          batch = metadata$batch,
                          mod = mod,
                          par.prior = TRUE)

适用场景

  • 适合在correcting batch effects in expression data时使用。

不适用场景

  • Do not rely on this catalog entry alone ,用于 installation 或 maintenance details。

上游相关技能

  • differential-expression/deseq2-basics - DE with batch in design
  • single-cell/clustering - Integration methods
  • expression-matrix/matrix-operations - Data transformation

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