bio-crispr-screens-copy-number-correction
Corrects the gene-independent copy-number artifact in CRISPR-Cas9 screens (Aguirre 2016 / Munoz 2016 Cancer Discov) where amplified loci appear essential from DNA-damage burden of simultaneous cuts. Covers the p53-dependent G2-arrest mechanism, CRISPRcleanR (Iorio 2018) unsupervised pre-hoc correction, CERES (Meyers 2017) joint CN + gene-effect model, Chronos (Dempster 2021) DepMap-standard population-dynamics + CN model with lowest residual bias, the decision tree by data availability, the Spearman LFC-vs-CN diagnostic, focal-amplification examples (ERBB2 in HER2+, MYC in colorectal, FGFR1 in
npx skills add BioTender-max/awesome-bio-agent-skills --skill copy-number-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with: CRISPRcleanR 3.0+ (R/Bioconductor), Chronos 2.0+ (https://github.com/broadinstitute/chronos), CERES (legacy, superseded by Chronos), pandas 2.2+, numpy 1.26+, scipy 1.12+. Before using code patterns, verify installed versions match. If versions differ: - R: `packageVersion('CRISPRcleanR')`; `?ccr.CleanCN` - Python: `pip show chronos-cn`; `chronos --help` If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. ## Copy-Number Bias Correction in CRISPR Screens **"Correct copy-number artifacts in my cancer-cell-line screen"** -> Identify gene-independent depletion at amplified loci, apply CRISPRcleanR (pre-hoc, unsupervised, position-based) or Chronos (joint model, supervised with CN profile) to remove the artifact, then proceed to hit calling on corrected data. - R: `CRISPRcleanR::ccr.CleanCN()` for unsupervised pre-hoc correction (no CN profile required) - Python: Chronos (`chronos-cn`) for joint cell-population dynamics + CN modeling - Python: CERES (legacy, superseded by Chronos) ## The Copy-Number Artifact (Mechanism) **Agu
- Version Compatibility
- Copy-Number Bias Correction in CRISPR Screens
- The Copy-Number Artifact (Mechanism)
- Correction Method Decision Tree
- CRISPRcleanR (Iorio 2018) - Unsupervised Pre-Hoc
- Chronos (Dempster 2021) - Joint Population-Dynamics + CN Model
- CERES (Legacy, Superseded by Chronos)
- Detect Uncorrected CN Bias
- Reconciliation: When CN Correction Fails
- Apply CN Correction to Pipeline
- Failure Modes
- CRISPRcleanR removes legitimate essential signal
- Chronos fails on single-timepoint or single-cell-line data
- Spearman ρ still negative after CRISPRcleanR
What does the bio-crispr-screens-copy-number-correction skill do?
Corrects the gene-independent copy-number artifact in CRISPR-Cas9 screens (Aguirre 2016 / Munoz 2016 Cancer Discov) where amplified loci appear essential from DNA-damage burden of simultaneous cuts. Covers the p53-dependent G2-arrest mechanism, CRISPRcleanR (Iorio 2018) unsupervised pre-hoc correction, CERES (Meyers 2017) joint CN + gene-effect model, Chronos (Dempster 2021) DepMap-standard population-dynamics + CN model with lowest residual bias, the decision tree by data availability, the Spearman LFC-vs-CN diagnostic, focal-amplification examples (ERBB2 in HER2+, MYC in colorectal, FGFR1 in
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill copy-number-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.