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DepMap CRISPR gene effect (Chronos) analysis: sign convention for essentiality, per-gene NaN-safe Spearman correlation, data loading/alignment. For general NaN-safe correlation see nan-safe-correlation; for quality filtering see degenerate-input-filtering.
Passed 22 of 22 test cases with the skill, and 13 of 22 without it.
and 11 more in the full report
9 fixed13 passed either way0 still failing0 broke
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One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
I need a Python script to evaluate the relationship between gene expression levels and gene essentiality across cancer cell lines using DepMap files (`CRISPRGeneEffect.csv` and `OmicsExpressionProteinCodingGenesTPMLogp1BatchCorrected.csv`). Please process both datasets for common genes and cell lines, calculate Spearman correlations between expression and essentiality for each gene, and output a summary table listing gene correlation values along with count tallies of genes exceeding correlation thresholds of +0.6 and -0.6.
| Checked | Without skill | With skill |
|---|---|---|
| The script negates the CRISPR gene effect values (or multiplies by -1) when calculating correlation with essentiality. | Missed | Passed |
| The script computes Spearman correlation gene-by-gene using scipy.stats.spearmanr instead of pandas DataFrame bulk corrwith or rank methods. | Missed | Passed |
| The script applies a minimum valid observation count check (such as requiring at least 10 complete pairs) before computing correlation for each gene. | Missed | Passed |
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This guide covers the correct interpretation and analysis of DepMap CRISPR gene effect (Chronos) data. The most critical and common error in DepMap analyses is failing to negate the CRISPR scores when computing correlations with "essentiality." A secondary but equally damaging mistake is using bulk correlation shortcuts that mishandle per-gene NaN patterns. This guide provides the mandatory sign convention, the correct per-gene NaN-safe Spearman correlation implementation, and data loading/alignment procedures.
The CRISPR gene effect score (produced by the Chronos algorithm) quantifies how gene knockout affects cell viability:
The DepMap portal distributes these scores in the file CRISPRGeneEffect.csv. Each row is a cell line (DepMap ID, e.g., ACH-000001) and each column is a gene in the format GENE_NAME (ENTREZ_ID), e.g., A1BG (1).
Because negative raw scores indicate essentiality, any analysis that asks about "essentiality" or "dependency" requires negating the raw CRISPR scores:
-CRISPRGeneEffect (negated)If you correlate expression with raw CRISPR scores and find 3 genes with correlation <= -0.6 and 0 genes with correlation >= 0.6, then the correct answer for "genes with strong positive correlation with essentiality" is 3, not 0. The negative correlations with raw scores ARE the positive correlations with essentiality.
The standard DepMap data files use a consistent structure:
ACH-XXXXXX)GENE_NAME (ENTREZ_ID) formatOmicsExpressionProteinCodingGenesTPMLogp1BatchCorrected.csv) uses the same index/column format, enabling direct alignmentDifferent genes have different patterns of missing data across cell lines. This is because not all genes are screened in all cell lines, and quality control may remove specific gene-cell line combinations.
test cases passed, vs 13 / 22 without
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