StarSignDNA: Signature tracing for accurate representation of mutational processes

A Robust Tool for Mutational Signature Analysis

StarSignDNA is a powerful algorithm for mutational signature analysis that provides both efficient refitting of known signatures and de novo signature extraction. The tool excels at:

• Deciphering well-differentiated signatures linked to known mutagenic mechanisms
• Demonstrating strong associations with patient clinical features
• Providing robust statistical analysis and visualization
• Handling both single-sample and cohort analyses
• Supporting various input formats including VCF files

Quick Links

• License: MIT license
• Paper: Preprint
• Source Code: https://github.com/uio-bmi/StarSignDNA

Installation

You can install StarSignDNA in one of two ways:

1. Via PyPI (Recommended):

   pip install starsigndna
   

2. From Source:

   git clone https://github.com/uio-bmi/StarSignDNA
   cd StarSignDNA
   pip install -e .
   

Key Features

• Signature Refitting: Accurate refitting of known mutational signatures to new samples
• De Novo Discovery: Novel signature extraction from mutation catalogs
• Flexible Input: Support for both VCF files and mutation count matrices
• Statistical Robustness: Built-in bootstrapping and cross-validation
• Visualization: Comprehensive plotting functions for signature analysis
• Performance: Optimized algorithms for both small and large datasets
• Reference Genome Support: Built-in handling of various genome builds
• Enhanced CLI: Improved signature parsing supporting both comma and space-separated formats

Basic Usage

Get help on available commands:

starsigndna --help

Available Commands:

• count-mutation: Count mutation types in VCF files
• denovo: Perform de novo signature discovery
• refit: Refit known signatures to new samples

Signature Refitting

The refitting algorithm matches mutation patterns against known COSMIC signatures.

Basic Usage:

starsigndna refit <matrix_file> <signature_file> [OPTIONS]

Example with Specific Signatures:

starsigndna refit example_data/M_catalogue.txt example_data/COSMICv34.txt \
    --output-folder /test_result \
    --signature-names "SBS1,SBS3,SBS5,SBS6,SBS8"

Example with VCF Input:

starsigndna refit example_data/tcga_coad_single.vcf example_data/COSMICv34.txt \
    --output-folder /test_result  \
    --signature-names "SBS1,SBS3,SBS5,SBS6,SBS8" \
    --ref-genome GRCh38

Key Options:

• --ref_genome: Reference genome for VCF processing
• --n_bootstraps: Number of bootstrap iterations (default: 200)
• --opportunity_file: Custom mutation opportunity matrix
• --signature_names: Specific signatures to consider (minimum 5 signatures required)
• --n_iterations: Maximum optimization iterations (default: 1000)

Signature Names Format: The --signature-names parameter accepts both comma-separated and space-separated formats: * Comma-separated: "SBS1,SBS3,SBS5,SBS6,SBS8"

Expected Output:

The refit command generates several output files in the specified output folder:

For single sample analysis: * StarSign_refit_exposure_median_{run_name}.txt: Median exposure values across bootstrap iterations * StarSign_refit_exposure_Exposure_{run_name}.txt: Full exposure matrix from bootstrap analysis * StarSign_refit_exposure_Exposure_{run_name}.png: Violin plot of exposure distributions

For cohort analysis: * refit_{run_name}_threshold.txt: Exposure matrix after signature filtering * average_refit_{run_name}.txt: Average exposure values across samples * starsign_refit_top5_signatures_{run_name}.png: Bar plot of top 5 signatures by average exposure * starsign_refit_cohort_{run_name}.png: Violin plot showing exposure distributions across cohort

For VCF input: * matrix.csv: Generated mutation count matrix from VCF file

De Novo Signature Discovery

The de novo algorithm extracts novel signatures from mutation patterns.

Basic Usage:

starsigndna denovo <matrix_file> <n_signatures> [OPTIONS]

Example with Optimal Parameters:

starsigndna denovo snakemake/results/data/M_catalogue.txt 4  \
    --cosmic-file example_data/COSMICv34.txt \
    --output-folder /test_result

In this example, we are using the default parameters for the de novo algorithm.

Parameter Optimization with Snakemake

For systematic parameter optimization and cross-validation, StarSignDNA includes a Snakemake workflow that performs grid search over multiple parameters.

Prerequisites: * Install Snakemake: pip install snakemake * Ensure you have sufficient computational resources

Workflow Steps:

1. Navigate to Snakemake Directory:

   cd snakemake
   

2. Configure Grid Search Parameters:

   # Edit the Snakefile to set your desired parameter ranges
   vi Snakefile
   
   # Key parameters to modify:
   ks = list(range(2, 10))              # Number of signatures to test
   lambdas = [0, 0.01, 0.05, 0.1, 0.2]  # Regularization parameters
   
   # For quick testing, use smaller ranges:
   ks = list(range(2, 3))               # Test fewer signatures
   lambdas = [0, 0.01]                  # Test fewer lambda values
   

3. Prepare Input Data:

   # Ensure your mutation matrix is in the correct location
   # Default expected location: results/data/train_m.csv and results/data/test_m.csv
   # Or modify the Snakefile to point to your data files
   

4. Run Grid Search:

   # Run with multiple CPU cores for faster processing
   snakemake -j 4                       # Use 4 CPU cores
   snakemake -j <num_cpu>               # Use specified number of cores
   
   # For dry run (check what will be executed):
   snakemake -n
   

5. Monitor Progress:

   # The workflow will show progress and create multiple output files
   # Results are stored in: results/data/<fold>/<k>/<lambda>/
   # Each combination generates: e.csv, s.csv, e_new.csv, logpmf.txt
   

6. Find Optimal Parameters:

   # Sort results by log-likelihood to find best parameters
   sort -k3n,3 results/data/all.csv | head -10
   
   # The output shows: fold, k, lambda, log-likelihood
   # Lower log-likelihood values indicate better model fit
   
   **Run de novo analysis with the optimal parameters:**
   
   starsigndna denovo example_data/M_catalogue.txt 4 --lambd 0.01  \
       --cosmic-file example_data/COSMICv34.txt \
       --output-folder /test_result
   

Understanding Snakemake Output:

• Cross-validation Results: Each parameter combination is tested across multiple folds
• Signature Files (s.csv): Extracted signatures for each parameter set
• Exposure Files (e.csv, e_new.csv): Training and test exposures
• Log-likelihood (logpmf.txt): Model fitness scores
• Summary (all.csv): Consolidated results across all parameter combinations

Key Options:

• --lambd: Regularization parameter (default: 0.7)
• --opportunity-file: Custom mutation opportunity matrix
• --cosmic-file: Reference signatures for comparison
• --max-em-iterations: Maximum EM iterations (default: 100)
• --max-gd-iterations: Maximum gradient descent steps (default: 50)

Expected Output:

The denovo command generates several output files in the specified output folder:

• StarSign_denovo_{run_name}_signature.txt: Extracted mutational signatures matrix (signatures × mutation types)
• StarSign_denovo_{run_name}_exposures.txt: Signature exposures for each sample (samples × signatures)
• StarSign_denovo_{run_name}_profile.png: Visualization of extracted signatures
• StarSign_denovo{run_name}_cosine_similarity.txt: Similarity scores with known COSMIC signatures (if --cosmic-file provided)

For VCF input: * matrix.csv: Generated mutation count matrix from VCF file

Contact

• Maintainer: Christian D. Bope
• Email: [email protected]
• Institution:University of Oslo/University of Kinshasa

Citation

If you use StarSignDNA in your research, please cite our paper: [Citation details to be added after publication]