Screen Qc

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/screen-qcThe 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: Quality control for pooled CRISPR screens covering library representation, Gini index, log-skew, replicate Pearson and Spearman concordance, essentialome precision-recall AUC against CEGv2 (Hart 2017), Cas9 cut-toxicity…

---
name: bio-crispr-screens-screen-qc
description: Quality control for pooled CRISPR screens covering library representation, Gini index, log-skew, replicate Pearson and Spearman concordance, essentialome precision-recall AUC against CEGv2 (Hart 2017), Cas9 cut-toxicity diagnostics, copy-number amplicon detection (Aguirre 2016 / Munoz 2016), bottleneck propagation through plasmid pool, infection, selection, and endpoint stages, MOI verification, and DepMap-style screen-quality scoring. Use when assessing screen quality before hit calling, deciding whether to repeat or rescue a screen, diagnosing low-confidence hits, choosing between MAGeCK / BAGEL2 / Chronos based on quality grade, picking a normalization strategy from QC signatures, or evaluating whether an in-vivo screen retained adequate library complexity.
tool_type: python
primary_tool: MAGeCK-VISPR
---

## Version Compatibility

Reference examples tested with: MAGeCK 0.5+ (count + VISPR), MAGeCKFlute 2.0+ (R), pandas 2.2+, numpy 1.26+, scikit-learn 1.4+, matplotlib 3.8+, seaborn 0.13+.

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

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

## CRISPR Screen Quality Control

**"Audit my CRISPR screen quality before hit calling"** -> Assess library representation, replicate concordance, depth, drift, and biological signal recovery using DepMap-grade metrics, then decide whether the screen is usable, salvageable, or must be repeated.

- Python: `pandas` + `scikit-learn` for Gini, AUC, PCA; `MAGeCKFlute` (R) for one-shot QC dashboard
- CLI: `mageck count` writes Gini and mapping stats unconditionally to `<prefix>.countsummary.txt` (`GiniIndex`, `Reads`, `Mapped`, `Percentage`); MAGeCK-VISPR for an interactive dashboard

## QC Stage Hierarchy

A pooled screen has six distinct bottlenecks where complexity can collapse. Audit each:

| Stage | Metric | Acceptable threshold | Failure consequence |
|-------|--------|----------------------|----------------------|
| Plasmid pool | Gini, skew, % zero-count guides | Gini <0.1, skew <2 (Joung 2017 states <10), zero <0.5% | Missing guides cannot be screened; dropout indistinguishable from non-coverage |
| Day-0 infection | Library coverage, MOI verification | ≥99% guide detection at 500x cells/sgRNA; MOI 0.3 | Founder effects; polyclonality with high MOI |
| Selection (puro/blast) | % cells surviving, time-course Gini | 30-40% survival at 5-7 days; Gini drift <0.05 | Selection artifact; fast-growers enriched |
| Endpoint | Replicate correlation, depth | Pearson >=0.8 on log-counts (MAGeCK-VISPR floor), Spearman >0.7-0.8, >500 reads/sgRNA (Joung 2017 screening) | Noise dominates; FDR inflates |
| Biological signal | CEGv2 PR-AUC, NEGv1 false-positive rate | PR-AUC >0.7 at FDR 5% (community "passing" convention); CEGv2 enrichment in top 1k | Screen lacks essentiality signal; hits not credible |
| Copy-number artifact | Amplified-region enrichment, sgRNA-cut-count correlation | No correlation between sgRNA off-target count and depletion | False-positive essentiality at amplicons; ERBB2 in HER2+ etc. |

Each metric below quantifies one of these stages.

## Library Representation Metrics

**Goal:** Detect dropout, oversaturation, and library bottlenecks at each sequencing stage.

**Approach:** Compute per-sample zero-count fraction, low-count fraction (<30 reads, the CRISPRcleanR `ccr.NormfoldChanges` default), and percentile-based skew, then track how these change between plasmid -> Day-0 -> endpoint to localize the bottleneck.

```python
import pandas as pd
import numpy as np

def library_representation(counts_df):
    '''Per-sample library coverage diagnostics.
    counts_df: rows = sgRNAs, columns = samples (numeric counts).'''
    out = pd.DataFrame(index=counts_df.columns)
    out['n_sgrnas_detected'] = (counts_df > 0).sum()
    out['pct_zero'] = (counts_df == 0).sum() / len(counts_df) * 100
    out['pct_lowcount'] = (counts_df < 30).sum() / len(counts_df) * 100
    out['median_count'] = counts_df.median()
    out['p10_count'] = counts_df.quantile(0.10)
    out['p90_count'] = counts_df.quantile(0.90)
    out['skew_ratio'] = out['p90_count'] / out['p10_count'].replace(0, np.nan)
    return out

def stage_specific_thresholds():
    '''Stage conventions: Joung 2017 (zero-count, skew) + MAGeCK-VISPR (Gini).'''
    return {
        'plasmid':  {'pct_zero_max': 0.5, 'skew_max': 2.0, 'gini_max': 0.10},   # skew 2.0 is a stricter modern convention; Joung 2017 states <10
        'day_0':    {'pct_zero_max': 1.0, 'skew_max': 2.5, 'gini_max': 0.12},
        'endpoint': {'pct_zero_max': 5.0, 'skew_max': 10.0, 'gini_max': 0.30},
    }
```

**Interpretation:** Plasmid pool failing Gini <0.1 indicates synthesis or amplification bias; the screen is unfit for use. Endpoint Gini drifting above 0.30 indicates either heavy biological selection (acceptable for strong-phenotype drug screens) or a bottleneck (must be diagnosed). The Day-0 vs plasmid delta isolates whether the issue arose during infection (cloning is unlikely to lose specific guides between extraction and infection -- the change happens in cells).

## Gini Coefficient

**Goal:** Quantify how unevenly reads are distributed across sgRNAs in a single sample.

**Approach:** Sort non-zero counts ascending, compute Gini via the cumulative-fraction formula. Compare against stage-specific thresholds.

```python
def gini(x):
    '''Gini coefficient: 0 = perfect equality, 1 = maximal inequality.
    Uses non-zero counts only; zero-count sgRNAs handled separately by % zero.'''
    x = np.sort(x[x > 0].astype(float))
    if x.size == 0:
        return np.nan
    n = x.size
    cumx = np.cumsum(x)
    return (n + 1 - 2 * np.

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.

pip install pandas numpy scipy matplotlib seaborn scikit-learn

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 Screen Qc

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

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