Agent skill · Data & Analytics

bio-differential-expression-batch-correction

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.

FreedomIntelligencegithub.com/FreedomIntelligenceGitHub ↗
claude-code
Install
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-differential-expression-batch-correction --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 3
SKILL.md size: 8 KB
Bundled scripts: none
Path: skills/bio-differential-expression-batch-correction/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 2,909
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

## Version Compatibility Reference examples tested with: DESeq2 1.42+, ggplot2 3.5+, limma 3.58+, scanpy 1.10+ 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. # Batch Effect Correction ## 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. ```r library(sva) # counts: raw count matrix (genes x samples) # batch: vector of batch labels # group: vector of biological condition (optional, to preserve) corrected_counts <- ComBat_seq(counts = as.matrix(counts), batch = batch, group = condition, full_mod = TRUE) # Result is batch-corrected count matrix # Use for visualizat

What's inside
Steps it walks through
  1. Version Compatibility
  2. ComBat-Seq (Count Data)
  3. ComBat (Normalized Data)
  4. limma removeBatchEffect
  5. DESeq2 Design Formula (Recommended for DE)
  6. Surrogate Variable Analysis (SVA)
  7. SVA with DESeq2
  8. Visualize Batch Effects
  9. Quantify Batch Effect
  10. Harmony (Single-Cell Integration)
  11. When NOT to Correct
  12. Related Skills
Ships with 2 files
  • examples/batch_correction_workflow.R
  • usage-guide.md
More from OpenClaw-Medical-Skills
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About this skill
What does the bio-differential-expression-batch-correction skill do?

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.

How do I install it?

Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-differential-expression-batch-correction --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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