bio-crispr-screens-batch-correction
Batch effect correction for CRISPR screens covering ComBat empirical-Bayes, RUV, SVA, control-sgRNA normalization, and the model-based alternative of including batch as a covariate in MAGeCK MLE or Chronos. Covers screen-specific batch sources (passage cohort, library lot, infection day, sequencing run, Cas9 lot, FBS lot), PCA + variance-decomposition diagnostic to decide if correction is needed, when correction harms biology by over-correcting condition into batch, limma removeBatchEffect for visualization-only correction, and relationship to multi-condition design matrices. Use when combinin
npx skills add BioTender-max/awesome-bio-agent-skills --skill 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: pyComBat 0.3.3+ (epigenelabs/pyComBat), MAGeCK 0.5.9+, R/limma 3.58+, sva 3.50+, RUVSeq 1.36+, pandas 2.2+, numpy 1.26+, scikit-learn 1.4+, scipy 1.12+. Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show pycombat`; `from combat.pycombat import pycombat` - R: `packageVersion('sva')`; `?ComBat`; `packageVersion('RUVSeq')`; `?RUVg` If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. ## Batch Correction for CRISPR Screens **"Correct batch effects in my CRISPR screens"** -> Diagnose the batch source, decide whether to remove via empirical-Bayes (ComBat), explicit covariate modeling (MAGeCK MLE / Chronos design matrix), control-guide-anchored normalization, or unwanted-variation decomposition (RUV, SVA), then apply only the correction that preserves biological condition signal. - Python: `pyComBat.pycombat` for empirical-Bayes correction - Python: explicit batch covariates in `mageck mle --design-matrix` - R: `sva::ComBat`, `RUVSeq::RUVg`, `limma::removeBatchEffect` - CLI: `chr
- Version Compatibility
- Batch Correction for CRISPR Screens
- Batch Sources in CRISPR Screens
- Batch Effect Decision Tree
- Diagnose: PCA + Variance Decomposition
- ComBat Empirical-Bayes Correction
- RUV (Remove Unwanted Variation)
- SVA (Surrogate Variable Analysis)
- Batch as Explicit Covariate (Preferred for MAGeCK MLE / Chronos)
- Control-Sgrna Anchored Normalization
- When NOT to Correct
- Failure Modes
- ComBat eliminates biological signal
- RUV adds noise instead of removing it
Design matrix for a screen with 2 batches and 2 conditions cat > design.txt <<EOF mageck mle \
What does the bio-crispr-screens-batch-correction skill do?
Batch effect correction for CRISPR screens covering ComBat empirical-Bayes, RUV, SVA, control-sgRNA normalization, and the model-based alternative of including batch as a covariate in MAGeCK MLE or Chronos. Covers screen-specific batch sources (passage cohort, library lot, infection day, sequencing run, Cas9 lot, FBS lot), PCA + variance-decomposition diagnostic to decide if correction is needed, when correction harms biology by over-correcting condition into batch, limma removeBatchEffect for visualization-only correction, and relationship to multi-condition design matrices. Use when combinin
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill 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.