Hit Calling

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha c8c1fc3a2b13 · 19 Oct 2026Report an issue or request a re-scan

• PublisherGPTomics GitHub organizationOwner of the repository this skill is read from
• Source folderGPTomics/bioSkills/crispr-screens/hit-callingThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdictc8c1fc3a2b13A new audit runs when the source changes
• Repository statusArchivedLast push 65 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: 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…

---
name: bio-crispr-screens-hit-calling
description: 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 methods on the same screen, deciding whether to require consensus, gating downstream validation by hit-confidence tier, or interpreting unstable hit lists across reruns.
tool_type: mixed
primary_tool: MAGeCK
---

## Version Compatibility

Reference examples tested with: MAGeCK 0.5.9+, BAGEL2 2.0, 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`, `python drugz.py --help`
- Python: `pip show crispr_chronos` (JACKS installs from GitHub, not PyPI)

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 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 analysis | BAGEL2 (Bayes factor on same data) |
| Time course (3+ timepoints) | MAGeCK MLE | RRA cannot model multi-condition | JACKS (efficacy-aware) |
| Multi-cell-line panel (cancer dependency) | Chronos | Models CN bias + screen quality jointly | MAGeCK MLE per line + meta-analysis |
| Drug screen (vehicle vs drug) | drugZ | Bidirectional Z; vehicle-anchored | MAGeCK MLE with dose covariate |
| Multi-screen joint, same library | JACKS | Shared efficacy; enables ~2.5x smaller screens | MAGeCK MLE; results should converge |
| Essentiality classification with reference sets | BAGEL2 | Bayes factor with CEGv2/NEGv1 calibration | MAGeCK RRA |
| Combinatorial / paired guide | MAGeCK MLE with GI scoring | Models interaction term; see [[combinatorial-screens]] | Custom GI scoring |
| Single-cell perturbation (Perturb-seq) | SCEPTRE | NB GLM + permutation; see [[perturb-seq-analysis]] | Mixscape pre-filter |
| Cancer-line copy-number screen | Chronos (preferred) or CERES | Joint CN-bias + gene-effect modeling; see [[copy-number-correction]] | CRISPRcleanR pre-hoc + MAGeCK |

## Statistical Models Compared

| Method | Year | Statistical model | Tests | Best for | Fails when |
|--------|------|-------------------|-------|----------|------------|
| MAGeCK RRA | 2014 | NB per-sgRNA -> alpha-RRA per gene | Two-sided | General two-condition | >40% guides change (median norm breaks); time course; cancer-line CN |
| MAGeCK MLE | 2015 | NB GLM with design matrix; per-gene beta | Wald per condition | Multi-condition / time course | Cell-line specific essentiality; CN bias |
| BAGEL2 | 2021 | Bayes factor from log-likelihood ratio | Essential vs non-essential | Essentiality classification | Non-essentiality screens; drug screens |
| drugZ | 2019 | Bidirectional Z-score on guide-level LFC | Sensitizer vs suppressor | Drug-modifier / chemogenomic | Essentiality (no biological prior); time-course |
| JACKS | 2019 | Variational Bayes: LFC = gene * efficacy | Per-gene posterior | Multi-screen joint, library calibration | Single screen; cross-chemistry |
| Chronos | 2021 | Cell-population dynamics ODE + NB | Gene effect adjusted for screen quality | Cancer-line panels, longitudinal | Single screen; non-cancer applications |
| CERES | 2017 | Nonlinear model decoupling CN-bias from gene effect | Per-gene effect | Cancer-line panel with CN profile | Superseded by Chronos at DepMap |

## RRA vs MLE Within MAGeCK

| Property | RRA (`mageck test`) | MLE (`mageck mle`) |
|----------|----------------------|---------------------|
| Conditions supported | 2 | Multiple (design matrix) |
| Statistical test | Robust rank aggregation | Wald on beta from NB GLM |
| Output | neg/pos score, FDR per direction | beta per condition |
| sgRNA efficiency | Not modeled (optional fixed input) | Modeled via `--sgrna-efficiency` |
| Outlier robustness | High (rank-based) | Lower (likelihood-based) |
| Best for | Standard 2-condition screen | Time course, drug screen, multi-cell-line, paired |
| Speed | Fast | Slow (per-gene optimization) |

## Algorithmic Taxonomy: Why Each Was Built

| Method | Designed to solve |
|--------|--------------------|
| MAGeCK RRA | First robust statistical framework for CRISPR-screen ranking; alpha-RRA borrowed from RRA in microarray meta-analysis |
| MAGeCK MLE | Extend MAGeCK to multi-condition; explicit beta scores allow direct LFC interpretation |
| BAGEL2 | Reference-set-anchored Bayesian classification; precision-recall calibrated; tumor-suppressor sensitivity (BAGEL1 was uni-directional) |
| drugZ | Drug-modifier screens have low effect sizes and need bidirectional sensitivity; STARS/MAGeCK miss synthetic-lethal hits |
| JACKS | Sample-size reduction via library-shared efficacy; library calibration as side product |
| Chronos | DepMap-scale (1000+ cell lines, billions of cell-divisions) needs population-dynamics model; CN bias

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/hart-lab/bagel          # BAGEL2 (PyPI 'bagel' is an unrelated package)
git clone https://github.com/felicityallen/JACKS     # JACKS (PyPI 'jacks' is an unrelated package)
git clone https://github.com/hart-lab/drugz          # drugZ is not on PyPI
git clone https://github.com/hart-lab/drugz
pip install crispr_chronos
pip install pandas numpy scipy statsmodels matplotlib seaborn

git clone https://github.com/GPTomics/bioSkills
git -C bioSkills checkout c8c1fc3a2b13

Works in

1. 2026-10-19
   ✓SAFE B 89 / 100

   first audit · source c8c1fc3a2b13

Questions about Hit Calling

• github.com/GPTomics/bioSkills/tree/main/crispr-screens/hit-calli — the audited source at commit c8c1fc3a2b13 and every finding aboveObserved · 19 Oct 2026

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