bio-crispr-screens-hit-calling
Cross-method decision tree for calling hits in pooled CRISPR screens. Catalogs statistical models (MAGeCK RRA, MAGeCK MLE, BAGEL2, drugZ, JACKS, Chronos, CERES), experimental designs each is built for, failure modes outside design domain, reconciliation when methods disagree, multiple-testing and effect-size thresholds, the order of operations (count -> QC -> CN-correct -> hit-call -> validate), the second-best-sgRNA conservative rule, and consensus-hit strategy. Use when choosing among MAGeCK / BAGEL2 / drugZ / JACKS / Chronos for a given design, reconciling disagreement across two or three m
npx skills add BioTender-max/awesome-bio-agent-skills --skill hit-calling --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: MAGeCK 0.5.9+, BAGEL2 1.0+, drugZ Aug 2019+, JACKS 0.2.0+, Chronos 2.0+ (DepMap), CERES 1.0+, pandas 2.2+, numpy 1.26+, scipy 1.12+, statsmodels 0.14+. Before using code patterns, verify installed versions match. If versions differ: - CLI: `mageck --version`, `BAGEL.py --version`, `drugz --help` - Python: `pip show jacks chronos-cn` If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. ## Hit Calling Decision Tree **"Identify significant hits in my CRISPR screen"** -> Choose the analysis method that matches the experimental design, statistical assumptions, and quality grade of the screen. Reconcile across methods when high-stakes hits must be validated. The five primary hit-calling methods cover non-overlapping niches; the decision is not "which is best" but "which matches the design." | Design / question | Primary method | Why | Secondary check | |--------------------|----------------|-----|------------------| | Two-condition essentiality, one cell line, no CN concerns | MAGeCK RRA | Robust, fast, gold-standard for ranked
- Version Compatibility
- Hit Calling Decision Tree
- Statistical Models Compared
- RRA vs MLE Within MAGeCK
- Algorithmic Taxonomy: Why Each Was Built
- Run All Five on the Same Data (Consensus Strategy)
- Reconciliation: When Two Methods Disagree
- Second-Best sgRNA Conservative Rule
- Multiple-Testing Correction Conventions
- Order of Operations
- Custom z-score Hit Calling (when standard tools don't fit)
- Failure Modes
- MAGeCK and BAGEL2 disagree by 200+ hits at FDR 0.05
- Chronos and MAGeCK disagree at the top 10 in a cancer line
What does the bio-crispr-screens-hit-calling skill do?
Cross-method decision tree for calling hits in pooled CRISPR screens. Catalogs statistical models (MAGeCK RRA, MAGeCK MLE, BAGEL2, drugZ, JACKS, Chronos, CERES), experimental designs each is built for, failure modes outside design domain, reconciliation when methods disagree, multiple-testing and effect-size thresholds, the order of operations (count -> QC -> CN-correct -> hit-call -> validate), the second-best-sgRNA conservative rule, and consensus-hit strategy. Use when choosing among MAGeCK / BAGEL2 / drugZ / JACKS / Chronos for a given design, reconciling disagreement across two or three m
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill hit-calling --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.