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SKILL.md
DepMap — Cancer Dependency Map
Overview
The Cancer Dependency Map (DepMap) project, run by the Broad Institute, systematically characterizes genetic dependencies across hundreds of cancer cell lines using genome-wide CRISPR knockout screens (DepMap CRISPR), RNA interference (RNAi), and compound sensitivity assays (PRISM). DepMap data is essential for:
Identifying which genes are essential for specific cancer types
def get_gene_dependency(gene_symbol, dataset="Chronos_Combined"):
"""Get CRISPR dependency scores for a gene across all cell lines."""
url = f"{BASE_URL}/gene"
params = {
"gene_id": gene_symbol,
"dataset": dataset
}
response = requests.get(url, params=params)
return response.json()
# Alternatively, use the /data endpoint:
def get_dependencies_slice(gene_symbol, dataset_name="CRISPRGeneEffect"):
"""Get a gene's dependency slice from a dataset."""
url = f"{BASE_URL}/data/gene_dependency"
params = {"gene_name": gene_symbol, "dataset_name": dataset_name}
response = requests.get(url, params=params)
data = response.json()
return data
3. Download-Based Analysis (Recommended for Large Queries)
For large-scale analysis, download DepMap data files and analyze locally:
import pandas as pd
import requests, os
def download_depmap_data(url, output_path):
"""Download a DepMap data file."""
response = requests.get(url, stream=True)
with open(output_path, 'wb') as f:
for chunk in response.iter_content(chunk_size=8192):
f.write(chunk)
# DepMap 24Q4 data files (update version as needed)
FILES = {
"crispr_gene_effect": "https://figshare.com/ndownloader/files/...",
# OR download from: https://depmap.org/portal/download/all/
# Files available:
# CRISPRGeneEffect.csv - Chronos gene effect scores
# OmicsExpressionProteinCodingGenesTPMLogp1.csv - mRNA expression
# OmicsSomaticMutationsMatrixDamaging.csv - mutation binary matrix
# OmicsCNGene.csv - copy number
# sample_info.csv - cell line metadata
}
def load_depmap_gene_effect(filepath="CRISPRGeneEffect.csv"):
"""
Load DepMap CRISPR gene effect matrix.
Rows = cell lines (DepMap_ID), Columns = genes (Symbol (EntrezID))
"""
df = pd.read_csv(filepath, index_col=0)
# Rename columns to gene symbols only
df.columns = [col.split(" ")[0] for col in df.columns]
return df
def load_cell_line_info(filepath="sample_info.csv"):
"""Load cell line metadata."""
return pd.read_csv(filepath)
4. Identifying Selective Dependencies
import numpy as np
import pandas as pd
def find_selective_dependencies(gene_effect_df, cell_line_info, target_gene,
cancer_type=None, threshold=-0.5):
"""Find cell lines selectively dependent on a gene."""
# Get scores for target gene
if target_gene not in gene_effect_df.columns:
return None
scores = gene_effect_df[target_gene].dropna()
dependent = scores[scores <= threshold]
# Add cell line info
result = pd.DataFrame({
"DepMap_ID": dependent.index,
"gene_effect": dependent.values
}).merge(cell_line_info[["DepMap_ID", "cell_line_name", "primary_disease", "lineage"]])
if cancer_type:
result = result[result["primary_disease"].str.contains(cancer_type, case=False, na=False)]
return result.sort_values("gene_effect")
# Example usage (after loading data)
# df_effect = load_depmap_gene_effect("CRISPRGeneEffect.csv")
# cell_info = load_cell_line_info("sample_info.csv")
# deps = find_selective_dependencies(df_effect, cell_info, "KRAS", cancer_type="Lung")
5. Biomarker Analysis (Gene Effect vs. Mutation)
import pandas as pd
from scipy import stats
def biomarker_analysis(gene_effect_df, mutation_df, target_gene, biomarker_gene):
"""
Test if mutation in biomarker_gene predicts dependency on target_gene.
Args:
gene_effect_df: CRISPR gene effect DataFrame
mutation_df: Binary mutation DataFrame (1 = mutated)
target_gene: Gene to assess dependency of
biomarker_gene: Gene whose mutation may predict dependency
"""
if target_gene not in gene_effect_df.columns or biomarker_gene not in mutation_df.columns:
return None
# Align cell lines
common_lines = gene_effect_df.index.intersection(mutation_df.index)
scores = gene_effect_df.loc[common_lines, target_gene].dropna()
mutations = mutation_df.loc[scores.index, biomarker_gene]
mutated = scores[mutations == 1]
wt = scores[mutations == 0]
stat, pval = stats.mannwhitneyu(mutated, wt, alternative='less')
return {
"target_gene": target_gene,
"biomarker_gene": biomarker_gene,
"n_mutated": len(mutated),
"n_wt": len(wt),
"mean_effect_mutated": mutated.mean(),
"mean_effect_wt": wt.mean(),
"pval": pval,
"significant": pval < 0.05
}
6. Co-Essentiality Analysis
import pandas as pd
def co_essentiality(gene_effect_df, target_gene, top_n=20):
"""Find genes with most correlated dependency profiles (co-essential partners)."""
if target_gene not in gene_effect_df.columns:
return None
target_scores = gene_effect_df[target_gene].dropna()
correlations = {}
for gene in gene_effect_df.columns:
if gene == target_gene:
continue
other_scores = gene_effect_df[gene].dropna()
common = target_scores.index.intersection(other_scores.index)
if len(common) < 50:
continue
r = target_scores[common].corr(other_scores[common])
if not pd.isna(r):
correlations[gene] = r
corr_series = pd.Series(correlations).sort_values(ascending=False)
return corr_series.head(top_n)
# Co-essential genes often share biological complexes or pathways
Query Workflows
Workflow 1: Target Validation for a Cancer Type
Download CRISPRGeneEffect.csv and sample_info.csv
Filter cell lines by cancer type
Compute mean gene effect for target gene in cancer vs. all others
Calculate selectivity: how specific is the dependency to your cancer type?
Cross-reference with mutation, expression, or CNA data as biomarkers
Workflow 2: Synthetic Lethality Screen
Identify cell lines with mutation/deletion in gene of interest (e.g., BRCA1-mutant)
Compute gene effect scores for all genes in mutant vs. WT lines
Identify genes significantly more essential in mutant lines (synthetic lethal partners)
Filter by selectivity and effect size
Workflow 3: Compound Sensitivity Analysis
Download PRISM compound sensitivity data (primary-screen-replicate-treatment-info.csv)
Correlate compound AUC/log2(fold-change) with genomic features
Identify predictive biomarkers for compound sensitivity
