claude-scientific-skills/scientific-skills/depmap/references/dependency_analysis.md

DepMap Dependency Analysis Guide

Understanding Chronos Scores

Chronos is the current (v5+) algorithm for computing gene dependency scores from CRISPR screen data. It addresses systematic biases including:

• Copy number effects (high-copy genes appear essential due to DNA cutting)
• Guide RNA efficiency variation
• Cell line growth rates

Score Interpretation

Score Range	Interpretation
> 0	Likely growth-promoting when knocked out (some noise)
0 to −0.3	Non-essential: minimal fitness effect
−0.3 to −0.5	Mild dependency
−0.5 to −1.0	Significant dependency
< −1.0	Strong dependency (common essential range)
≈ −1.0	Median of pan-essential genes (e.g., proteasome subunits)

Common Essential Genes (Controls)

Genes that are essential in nearly all cell lines (score ~−1 to −2):

• Ribosomal proteins: RPL..., RPS...
• Proteasome: PSMA..., PSMB...
• Spliceosome: SNRPD1, SNRNP70
• DNA replication: MCM2, PCNA
• Transcription: POLR2A, TAF...

These can be used as positive controls for screen quality.

Non-Essential Controls

Genes with negligible fitness effect (score ~ 0):

• Non-expressed genes (tissue-specific)
• Safe harbor loci

Selectivity Assessment

To determine if a dependency is cancer-selective:

import pandas as pd
import numpy as np

def compute_selectivity(gene_effect_df, target_gene, cancer_lineage):
    """Compute selectivity score for a cancer lineage."""
    scores = gene_effect_df[target_gene].dropna()

    # Get cell line metadata
    from depmap_utils import load_cell_line_info
    cell_info = load_cell_line_info()
    scores_df = scores.reset_index()
    scores_df.columns = ["DepMap_ID", "score"]
    scores_df = scores_df.merge(cell_info[["DepMap_ID", "lineage"]])

    cancer_scores = scores_df[scores_df["lineage"] == cancer_lineage]["score"]
    other_scores = scores_df[scores_df["lineage"] != cancer_lineage]["score"]

    # Selectivity: lower mean in cancer lineage vs others
    selectivity = other_scores.mean() - cancer_scores.mean()
    return {
        "target_gene": target_gene,
        "cancer_lineage": cancer_lineage,
        "cancer_mean": cancer_scores.mean(),
        "other_mean": other_scores.mean(),
        "selectivity_score": selectivity,
        "n_cancer": len(cancer_scores),
        "fraction_dependent": (cancer_scores < -0.5).mean()
    }

CRISPR Dataset Versions

Dataset	Description	Recommended
CRISPRGeneEffect	Chronos-corrected gene effect	Yes (current)
Achilles_gene_effect	Older CERES algorithm	Legacy only
RNAi_merged	DEMETER2 RNAi	For cross-validation

Quality Metrics

DepMap reports quality control metrics per screen:

• Skewness: Pan-essential genes should show negative skew
• AUC: Area under ROC for pan-essential vs non-essential controls

Good screens: skewness < −1, AUC > 0.85

Cancer Lineage Codes

Common values for lineage field in sample_info.csv:

Lineage	Description
lung	Lung cancer
breast	Breast cancer
colorectal	Colorectal cancer
brain_cancer	Brain cancer (GBM, etc.)
leukemia	Leukemia
lymphoma	Lymphoma
prostate	Prostate cancer
ovarian	Ovarian cancer
pancreatic	Pancreatic cancer
skin	Melanoma and other skin
liver	Liver cancer
kidney	Kidney cancer

Synthetic Lethality Analysis

import pandas as pd
import numpy as np
from scipy import stats

def find_synthetic_lethal(gene_effect_df, mutation_df, biomarker_gene,
                           fdr_threshold=0.1):
    """
    Find synthetic lethal partners for a loss-of-function mutation.

    For each gene, tests if cell lines mutant in biomarker_gene
    are more dependent on that gene vs. WT lines.
    """
    if biomarker_gene not in mutation_df.columns:
        return pd.DataFrame()

    # Get mutant vs WT cell lines
    common = gene_effect_df.index.intersection(mutation_df.index)
    is_mutant = mutation_df.loc[common, biomarker_gene] == 1

    mutant_lines = common[is_mutant]
    wt_lines = common[~is_mutant]

    results = []
    for gene in gene_effect_df.columns:
        mut_scores = gene_effect_df.loc[mutant_lines, gene].dropna()
        wt_scores = gene_effect_df.loc[wt_lines, gene].dropna()

        if len(mut_scores) < 5 or len(wt_scores) < 10:
            continue

        stat, pval = stats.mannwhitneyu(mut_scores, wt_scores, alternative='less')
        results.append({
            "gene": gene,
            "mean_mutant": mut_scores.mean(),
            "mean_wt": wt_scores.mean(),
            "effect_size": wt_scores.mean() - mut_scores.mean(),
            "pval": pval,
            "n_mutant": len(mut_scores),
            "n_wt": len(wt_scores)
        })

    df = pd.DataFrame(results)
    # FDR correction
    from scipy.stats import false_discovery_control
    df["qval"] = false_discovery_control(df["pval"], method="bh")
    df = df[df["qval"] < fdr_threshold].sort_values("effect_size", ascending=False)
    return df

Drug Sensitivity (PRISM)

DepMap also contains compound sensitivity data from the PRISM assay:

import pandas as pd

def load_prism_data(filepath="primary-screen-replicate-collapsed-logfold-change.csv"):
    """
    Load PRISM drug sensitivity data.
    Rows = cell lines, Columns = compounds (broad_id::name::dose)
    Values = log2 fold change (more negative = more sensitive)
    """
    return pd.read_csv(filepath, index_col=0)

# Available datasets:
# primary-screen: 4,518 compounds at single dose
# secondary-screen: ~8,000 compounds at multiple doses (AUC available)