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.
npx skills add BioTender-max/awesome-bio-agent-skills --skill bioskills__differential-expression__batch-correction --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## 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
- Version Compatibility
- ComBat-Seq (Count Data)
- ComBat (Normalized Data)
- limma removeBatchEffect
- DESeq2 Design Formula (Recommended for DE)
- Surrogate Variable Analysis (SVA)
- SVA with DESeq2
- Visualize Batch Effects
- Quantify Batch Effect
- Harmony (Single-Cell Integration)
- Critical: When NOT to Use Corrected Counts
- Confounding and Limitations
- Related Skills
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 BioTender-max/awesome-bio-agent-skills --skill bioskills__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 BioTender-max/awesome-bio-agent-skills, a repository with 135 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.