Mageck Analysis

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/mageck-analysisThe 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: Analyzes pooled CRISPR screens with MAGeCK (Li et al 2014), covering count generation (mageck count), the RRA two-condition workflow (mageck test using alpha-RRA over per-sgRNA negative-binomial p-values), the MLE…

---
name: bio-crispr-screens-mageck-analysis
description: Analyzes pooled CRISPR screens with MAGeCK (Li et al 2014), covering count generation (mageck count), the RRA two-condition workflow (mageck test using alpha-RRA over per-sgRNA negative-binomial p-values), the MLE multi-condition workflow (mageck mle with explicit design matrix and beta-score output), normalization choice (median vs total vs control-sgRNA vs spike-in), sgRNA efficiency injection, paired-sample testing, time-course design, drug-screen versus dropout-screen design matrices, MAGeCKFlute and MAGeCK-VISPR downstream visualization, and decision logic for when to use MAGeCK vs JACKS / BAGEL2 / drugZ / Chronos. Use when running a fresh CRISPR screen analysis, picking RRA vs MLE for the experimental design, choosing a normalization method from QC signatures, debugging MLE convergence failure or NaN beta scores, comparing MAGeCK output across tools, or building a batch-aware multi-cell-line / multi-condition MLE design matrix.
tool_type: cli
primary_tool: MAGeCK
---

## Version Compatibility

Reference examples tested with: MAGeCK 0.5.9+, MAGeCKFlute 2.0+ (R/Bioconductor), MAGeCK-VISPR 0.5.6+, pandas 2.2+, numpy 1.26+, matplotlib 3.8+.

Before using code patterns, verify installed versions match. If versions differ:
- CLI: `mageck --version`, `mageck count --help`, `mageck test --help`, `mageck mle --help`
- R: `packageVersion('MAGeCKFlute')`, `?FluteRRA`, `?FluteMLE`

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

## MAGeCK CRISPR Screen Analysis

**"Run MAGeCK on my pooled CRISPR screen"** -> Count sgRNAs from FASTQ, normalize across samples, and rank genes by enrichment or depletion using either the robust rank aggregation (RRA) test for two-condition designs or the maximum-likelihood (MLE) model with explicit design matrix for multi-condition / time-course / drug screens.

- CLI: `mageck count` -> `mageck test` for two-condition RRA
- CLI: `mageck mle` for multi-condition / time-course / multi-cell-line MLE
- R: `MAGeCKFlute::FluteRRA()` / `FluteMLE()` for downstream visualization and pathway analysis
- Python: `mageck-vispr` for interactive QC + result dashboard

## RRA vs MLE Decision Tree

| Experimental design | Recommended | Why |
|---------------------|-------------|-----|
| Two conditions (e.g. drug vs vehicle, treated vs untreated), single cell line, no covariates | `mageck test` (RRA) | RRA is more robust to outlier sgRNAs; faster; default for most published screens |
| Time series (Day 0 -> Day 7 -> Day 14 -> Day 21) | `mageck mle` | RRA cannot model multiple timepoints jointly; MLE estimates per-condition beta scores |
| Multi-cell-line panel (e.g. DepMap-style 5-50 lines) | `mageck mle` with cell-line covariate, or Chronos | MLE handles >2 conditions; Chronos preferred at DepMap scale |
| Paired samples (each replicate matched donor/cell prep) | `mageck mle` with paired design | RRA does not support pairing |
| Combinatorial (treatment x cell line x time) | `mageck mle` with full factorial design | RRA only handles 1 factor |
| Drug screen (vehicle vs drug, multiple doses) | `mageck mle` with dose covariate OR drugZ (preferred for chemogenomic) | drugZ optimized for chemogenomic; see [[drugz-chemogenomic]] |
| Essentiality (Day 0 -> endpoint, single condition) | `mageck test` for simple dropout; `mageck mle` if multi-cell-line | RRA suffices; or BAGEL2 for Bayesian essentiality; see [[bagel-essentiality]] |
| Multi-batch / multi-screen joint analysis | `mageck mle` with batch covariate, JACKS for guide efficacy, or Chronos | See [[batch-correction]] |

**Fails when:**
- Using RRA for time-series and treating each timepoint as a separate test: false-discovery inflation from un-modeled temporal correlation. Use MLE.
- Using MLE without explicit design matrix in a screen with severe batch effects: beta scores include batch variance. Add batch covariate.
- Using MAGeCK for drug screens without vehicle control: comparing drug vs Day 0 conflates drug effect with proliferation. Compare drug vs vehicle, not Day 0. See [[drugz-chemogenomic]].

## The RRA Algorithm (under the hood)

**Why this matters for postdoc-level use:** RRA is not "non-parametric"; it tests whether per-gene sgRNA p-value ranks are more clustered toward the extremes than expected under the uniform null. The chain:

1. `mageck count` normalizes raw counts (median by default) and outputs `*.count_normalized.txt`.
2. `mageck test` computes a per-sgRNA p-value under a NB model fitted to per-gene variance (mean-variance trend stabilized via empirical Bayes shrinkage; same family as edgeR/DESeq2 but simpler dispersion estimation).
3. Per-sgRNA p-values are ranked; ranks are normalized to percentile (rho).
4. For each gene with k sgRNAs, the alpha-RRA score is the minimum over `Pr(beta(i, k-i+1) <= rho_i)` for i in 1..k -- the probability of observing the i-th smallest rank in a uniform sample.
5. Gene-level p-value is computed by permuting sgRNA-to-gene assignment to derive an empirical null over alpha-RRA scores.
6. Multiple-testing correction is Benjamini-Hochberg.

**Critical assumption:** RRA assumes most sgRNAs are non-changing (used to estimate the NB dispersion). If >40% of sgRNAs change, median normalization fails and the dispersion estimate is biased. Symptom: every gene appears significant. Fix: use control-sgRNA normalization (`--norm-method control`) with non-targeting controls as the reference.

## The MLE Model (under the hood)

**Why this matters:** MLE assumes the per-sgRNA log-count is Negative-Binomial with mean determined by a linear combination of condition betas plus an sgRNA-efficiency term (if supplied). The chain:

1. Define a design matrix where rows are samples and columns are conditions; entries are 1 if the sample belongs to the condition, 0 otherwise. A baseline column (all 1s) is required.
2. Per-gene, the model

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/GPTomics/bioSkills
git -C bioSkills checkout c8c1fc3a2b13

Works in

1. 2026-10-19
   ✓SAFE B 89 / 100

   first audit · source c8c1fc3a2b13

Questions about Mageck Analysis

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

Spotted something wrong? Report a correction · Methodology