mkurman/depmap

-|-------|---------| | 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 name
• primary_disease: cancer type
• lineage: broad tissue lineage
• lineage_subtype: specific subtype

Core Capabilities

1. DepMap API

python
import requests
import pandas as pd

BASE_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

python
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:

python
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

python
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)

python
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

python
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

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/

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

Additional Resources

• DepMap Portal: https://depmap.org/portal/
• Data downloads: https://depmap.org/portal/download/all/
• DepMap paper: Behan FM et al. (2019) Nature. PMID: 30971826
• Chronos paper: Dempster JM et al. (2021) Nature Methods. PMID: 34349281
• GitHub: https://github.com/broadinstitute/depmap-portal
• Figshare: https://figshare.com/articles/dataset/DepMap_24Q4_Public/27993966