erobeng-master
by @erisonbarros
Query the Cancer Dependency Map (DepMap) for cancer cell line gene dependency scores (CRISPR Chronos), drug sensitivity data, and gene effect profiles. Use f...
clawhub install erobeng-masterπ About This Skill
name: depmap description: Query the Cancer Dependency Map (DepMap) for cancer cell line gene dependency scores (CRISPR Chronos), drug sensitivity data, and gene effect profiles. Use for identifying cancer-specific vulnerabilities, synthetic lethal interactions, and validating oncology drug targets. license: CC-BY-4.0 metadata: skill-author: Kuan-lin Huang
DepMap β 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:
Key resources:
depmap (or access via API/downloads)When to Use This Skill
Use DepMap when:
Core Concepts
Dependency Scores
| Score | Range | Meaning | |-------|-------|---------| | Chronos (CRISPR) | ~ -3 to 0+ | More negative = more essential. Common essential threshold: β1. Pan-essential genes ~β1 to β2 | | RNAi DEMETER2 | ~ -3 to 0+ | Similar scale to Chronos | | Gene Effect | normalized | Normalized Chronos; β1 = median effect of common essential genes |
Key thresholds:
Cell Line Annotations
Each cell line has:
DepMap_ID: unique identifier (e.g., ACH-000001)cell_line_name: human-readable nameprimary_disease: cancer typelineage: broad tissue lineagelineage_subtype: specific subtypeCore Capabilities
1. DepMap API
import requests
import pandas as pdBASE_URL = "https://depmap.org/portal/api"
def depmap_get(endpoint, params=None):
url = f"{BASE_URL}/{endpoint}"
response = requests.get(url, params=params)
response.raise_for_status()
return response.json()
2. Gene Dependency Scores
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, osdef 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 pddef 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 statsdef 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 pddef 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
1. Download CRISPRGeneEffect.csv and sample_info.csv
2. Filter cell lines by cancer type
3. Compute mean gene effect for target gene in cancer vs. all others
4. Calculate selectivity: how specific is the dependency to your cancer type?
5. Cross-reference with mutation, expression, or CNA data as biomarkers
Workflow 2: Synthetic Lethality Screen
1. Identify cell lines with mutation/deletion in gene of interest (e.g., BRCA1-mutant) 2. Compute gene effect scores for all genes in mutant vs. WT lines 3. Identify genes significantly more essential in mutant lines (synthetic lethal partners) 4. Filter by selectivity and effect size
Workflow 3: Compound Sensitivity Analysis
1. Download PRISM compound sensitivity data (primary-screen-replicate-treatment-info.csv)
2. Correlate compound AUC/log2(fold-change) with genomic features
3. Identify predictive biomarkers for compound sensitivity
DepMap Data Files Reference
| File | Description |
|------|-------------|
| CRISPRGeneEffect.csv | CRISPR Chronos gene effect (primary dependency data) |
| CRISPRGeneEffectUnscaled.csv | Unscaled CRISPR scores |
| RNAi_merged.csv | DEMETER2 RNAi dependency |
| sample_info.csv | Cell line metadata (lineage, disease, etc.) |
| OmicsExpressionProteinCodingGenesTPMLogp1.csv | mRNA expression |
| OmicsSomaticMutationsMatrixDamaging.csv | Damaging somatic mutations (binary) |
| OmicsCNGene.csv | Copy number per gene |
| PRISM_Repurposing_Primary_Screens_Data.csv | Drug sensitivity (repurposing library) |
Download all files from: https://depmap.org/portal/download/all/