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
- Finding cancer-selective dependencies (therapeutic targets)
- Validating oncology drug targets
- Discovering synthetic lethal interactions
Key resources:
There is no DepMap Python package. The PyPI project named depmap is "Dependency Mapper CLI",
an unrelated software-dependency tool — installing it will not get you cell-line data. Read the
release CSVs with pandas.
When to Use This Skill
Use DepMap when:
- Target validation: Is a gene essential for survival in cancer cell lines with a specific mutation (e.g., KRAS-mutant)?
- Biomarker discovery: What genomic features predict sensitivity to knockout of a gene?
- Synthetic lethality: Find genes that are selectively essential when another gene is mutated/deleted
- Drug sensitivity: What cell line features predict response to a compound?
- Pan-cancer essentiality: Is a gene broadly essential across all cancer types (bad target) or selectively essential?
- Correlation analysis: Which pairs of genes have correlated dependency profiles (co-essentiality)?
Core Concepts
Dependency Scores
| Score | Range | Meaning |
|---|
| Chronos (CRISPR) | ~ -3 to 0+ | More negative = more essential. Common essential threshold: −1. Pan-essential genes ~−1 to −2 |
| RNAi DEMETER2 | ~ -3 to 0+ | Similar scale to Chronos |
| Gene Effect | normalized | Normalized Chronos; −1 = median effect of common essential genes |
Key thresholds:
- Chronos ≤ −0.5: likely dependent
- Chronos ≤ −1: strongly dependent (common essential range)
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 subtype
Core Capabilities
1. The portal API is not a usable data source
Checked live, August 2026: every path under https://depmap.org/portal/api/ answers a
programmatic request with HTTP 200 and an HTML browser-verification page, not JSON. That
combination is the trap — response.raise_for_status() sees the 200 and passes, so the failure
surfaces later as a JSONDecodeError, or worse as an HTML string quietly carried forward as data.
DepMap publishes no documented, stable, public REST API. Download the release files and work
locally; that is the supported route and the one the rest of this skill uses.
2. Download-Based Analysis (the supported route)
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
# Model.csv - cell line metadata (was sample_info.csv before 23Q2)
}
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="Model.csv"):
"""Load cell line metadata.
Release 23Q2 renamed this file from sample_info.csv AND renamed its columns:
DepMap_ID -> ModelID, cell_line_name -> CellLineName,
primary_disease -> OncotreePrimaryDisease, lineage -> OncotreeLineage.
Code written against the old names merges to an empty frame rather than
raising, so check the columns you actually got before trusting a join.
"""
return pd.read_csv(filepath)
3. 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"]])
# On a 23Q2+ Model.csv these are ModelID / CellLineName /
# OncotreePrimaryDisease / OncotreeLineage -- rename before merging.
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("Model.csv") # see the column renames in the loader
# deps = find_selective_dependencies(df_effect, cell_info, "KRAS", cancer_type="Lung")
4. 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
}
5. 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 Model.csv (cell-line metadata; this file was named
sample_info.csv before release 23Q2, and older code still asks for that name)
- 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
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 |
Model.csv | Cell line metadata (lineage, disease, etc.). Called sample_info.csv before 23Q2 |
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/
Read references/dependency_analysis.md before acting on a
score — it covers what Chronos corrects for, the full score-band interpretation, selectivity
metrics, and the copy-number and expression confounders that produce false dependencies.
Best Practices
- Use Chronos scores (not DEMETER2) for current CRISPR analyses — better controlled for cutting efficiency
- Distinguish pan-essential from cancer-selective: Target genes with low variance (essential in all lines) are poor drug targets
- Validate with expression data: A gene not expressed in a cell line will score as non-essential regardless of actual function
- Use DepMap ID for cell line identification — cell_line_name can be ambiguous
- Account for copy number: Amplified genes may appear essential due to copy number effect (junk DNA hypothesis)
- Multiple testing correction: When computing biomarker associations genome-wide, apply FDR correction
Composing with the rest of the bundle
open-targets → before: its depMapEssentiality roll-up is the summary of what is here. Come to
this skill when the roll-up says "essential" and you need to know in which lineages.target-safety → alongside: cell-line essentiality is not human tolerance. A gene essential
across the panel may still have healthy human knockouts — gnomAD LOEUF answers that, DepMap
cannot.chembl → after: once a dependency looks selective, what has already been made against it.uniprot-rcsb → after: the structure, once the target survives triage.clinicaltrials → after: whether anyone has taken this vulnerability into patients.primekg / ncats-arax → alongside: mechanistic context for a co-essentiality pair that has no
obvious pathway explanation.
A pan-essential gene is a toxicity finding, not a target. The whole point of the panel is the
contrast between lineages; a gene at −1 everywhere kills normal cells too.
Additional Resources