DepMap Gene Correlation Analysis Tool - MCP Server

A MCP tool from Prism ToolSpace for analyzing gene-gene correlations from the DepMap (Dependency Map) CRISPR knockout screening dataset. This tool processes systematic CRISPR-Cas9 knockout data from over 1,320 cancer cell lines from DepMap 24Q2 to identify genetic dependencies and co-essential gene pairs.

Prerequisites

1. Install Required Dependencies

Install the required Python packages for the DepMap correlation analysis:

# Create a virtual environment for DepMap setup
uv venv depmap --python 3.10
source depmap/bin/activate
uv pip install -r requirements.txt

Data Setup

1. Download DepMap 24Q2 Dataset

Download the preprocessed DepMap correlation data from the Prism ToolSpace or prepare your own correlation matrices:

# Install CLI if not already
uvx --from huggingface_hub hf

# Download only the depmap_24q2 folder
uvx --from huggingface_hub hf download mims-harvard/ToolSpace \
  --repo-type dataset \
  --include "depmap_24q2/*" \
  --local-dir ./path/to/your/depmap/

Required Files:

  • Gene correlation matrix - Pairwise correlations between genes

  • P-value matrix - Statistical significance of correlations

  • Gene index - Mapping of gene symbols to matrix indices

  • Adjusted p-values (optional) - FDR-corrected p-values

Data Sources:

  • DepMap Portal: https://depmap.org/portal/download/

  • DepMap 24Q2 Release: Contains CRISPR knockout data for 1,320+ cell lines

  • CERES Algorithm: Standardized gene effect scores for dependency analysis

2. Directory Structure Setup

Create the following directory structure for your DepMap data:

/path/to/your/depmap/
├── depmap_24q2/              # DepMap data directory
│   ├── corr_matrix.npy       # Gene correlation matrix (dense format)
│   ├── p_val_matrix.npy      # P-value matrix (dense format)
│   ├── p_adj_matrix.npy      # Adjusted p-values (optional)
│   ├── gene_idx_array.npy    # Gene symbol index array
│   └── gene_names.txt        # Gene symbols (alternative format)
│
│   # Alternative sparse format for large datasets:
│   └── gene_correlations.h5  # HDF5 sparse matrices

3. Set Environment Variable

Set the DEPMAP_DATA_PATH environment variable to point to your DepMap installation:

# Add to your ~/.bashrc or ~/.zshrc
export DEPMAP_DATA_PATH="/path/to/your/depmap"

Input and Output Specifications

Input Format

The tool accepts gene symbol pairs for correlation analysis:

  • Gene Symbols: Standard HUGO gene nomenclature (e.g., “BRAF”, “TP53”, “MAPK1”)

  • Case Insensitive: Tool automatically standardizes gene symbols

  • Validation: Checks gene availability in the correlation matrix

Output Format

The tool returns a structured JSON response with comprehensive correlation analysis:

{
  "correlation_data": {
    "correlation": 0.756,
    "p_value": 1.23e-15,
    "adjusted_p_value": 4.56e-12
  },
  "interpretation": {
    "strength": "strong",
    "significance": "significant (FDR corrected)",
    "direction": "similar",
    "biological_relationship": "co-dependent relationship (shared essential functions)",
    "summary": "DepMap analysis reveals a strong, similar correlation (r=0.756) in knockout effects between BRAF and MAPK1, suggesting co-dependent relationship (shared essential functions). This finding is significant (FDR corrected)."
  },
  ...
}

Output Fields:

  • correlation_data (dict): Statistical measures

    • correlation (float): Pearson correlation coefficient (-1.0 to 1.0)

    • p_value (float): Statistical significance of correlation

    • adjusted_p_value (float, optional): FDR-corrected p-value

  • interpretation (dict): Biological and statistical context

    • strength (str): Correlation strength classification

    • significance (str): Statistical significance interpretation

    • direction (str): Relationship type (similar vs opposing effects)

    • biological_relationship (str): Biological interpretation

    • summary (str): Comprehensive analysis summary

  • context_info (list): Analysis metadata and messages

  • error (str, optional): Error description if analysis failed

Running the MCP Server

1. Start the Server

# Activate the virtual environment
source depmap/bin/activate

# Set environment variable (if not in bashrc)
export DEPMAP_DATA_PATH="/path/to/your/depmap"

# Run the MCP server
python depmap_24q2_mcp_tool.py

2. Server Configuration

The server runs with the following default settings:

  • Host: 0.0.0.0 (accepts connections from any IP)

  • Port: 7002 (configured to avoid conflicts)

  • Transport: streamable-http

  • Mode: Stateless HTTP

Common Issues

  1. Data Directory Not Found

    FileNotFoundError: DepMap data directory not found at /path/to/data

    • Ensure DEPMAP_DATA_PATH is set correctly

    • Verify the depmap_24q2/ subdirectory exists

    • Check that correlation matrices are properly downloaded

  2. Gene Symbol Not Found

    KeyError: Gene 'INVALID' not available in the DepMap correlation matrix

    • Verify gene symbol spelling (use standard HUGO nomenclature)

    • Check if gene is present in the DepMap 24Q2 dataset

    • Try alternative gene symbols or aliases

  3. Missing Correlation Data

    FileNotFoundError: No correlation data found in directory

    • Ensure correlation matrices are in the correct format (.npy or .h5)

    • Verify gene index files are present (gene_idx_array.npy or gene_names.txt)

    • Check file permissions and accessibility

References