StarSignDNA algorithm for mutational signature analysis which offers efficient refitting and de novo mutational signature extraction. StarSignDNA is capable of deciphering well-differentiated signatures linked to known mutagenic mechanisms and demonstrates strong associations with patient clinical features. The package offers a user- friendly interface and data visualization routines.
- Free software: MIT license
- Documentation: Link to be provided
Preprint link https://www.biorxiv.org/content/10.1101/2024.06.29.601345v1
To install StarSign,
you can install the package directly from PyPi:
pip install starsigndna Alternatively, you can install it via terminal by running this command:: 1. Download StarSign from https://github.com/uio-bmi/StarSignDNA 2. Unzip StarSignDNA-master.zip 3. cd StarSigndna-master/ 4. pip install -e .
To obtain help:
starsigndna --help
Usage: starsigndna [OPTIONS] COMMAND [ARGS]...
- --install-completion: Install completion for the current shell.
- --show-completion: Show completion for the current shell, to copy it or customize the installation.
- --help: Show this message and exit.
- count-mutation: Count mutation types in VCF file.
- denovo: Performs denovo Mutational Signatures analysis.
- refit: Mutational Signatures Refit Parameters
The refitting algorithm takes as input a mutational catalog and a COSMIC mutational signature file. The user can also specify signatures to be considered instead of using the full COSMIC matrix or a subset matrix:
starsigndna refit --help
Arguments
- matrix_file (TEXT): Tab separated matrix file [default: None] [required]
- signature_file (TEXT): Tab separated matrix file [default: None] [required]
Options
- --ref_genome (TEXT): Path to the reference genome [default: None]
- --n_bootstraps (INTEGER): Number of bootstraps [default: 200]
- --opportunity_file (TEXT): Path to the opportunity file [default: None]
- --numeric_chromosomes: If set, chromosome names are numeric [default: no-numeric-chromosomes]
- --no_numeric_chromosomes: If set, chromosome names are not numeric [default: no-numeric-chromosomes]
- --genotyped: If set, VCF file has genotype information for many samples [default: genotyped]
- --no_genotyped: If set, VCF file does not have genotype information for many samples [default: genotyped]
- --output_folder (TEXT): Path to the output folder [default: output/]
- --signature_names (TEXT): Comma separated list of signature names [default: None]
- --n_iterations (INTEGER): Number of iterations [default: 1000]
- --help: Show this message and exit
Running StarSignDNA refit:
starsigndna refit example_data/M_catalogue.txt example_data/COSMICv34.txt --output-folder /test_result --signature-names SBS40c,SBS2,SBS94 starsigndna refit example_data/tcga_coad_single.vcf example_data/sig_cosmic_v3_2019.txt --output-folder /output --signature-names SBS40c,SBS2,SBS94 --ref-genome
When the --signature-names option is used, the default number of signature is 3, but we recommend minimum of 5 signatures. The test data is provided in the example_data folder. To convert *.vcf to a matrix, the user must provide the path to the reference genome using the option --ref-genome.
The user can also provide the distribution of triplets in a reference genome/exome or normal tissue in the same patient (Opportunity matrix) using the option --opportunity-file human-genome/human-exome.
The de novo algorithm takes as input a mutational catalog and infers the exposure matrix and mutational signature matrix. The COSMIC mutational signature file is provided to compute the cosine similarity:
starsigndna denovo --help
Arguments
- matrix_file (TEXT): Tab separated matrix file [default: None] [required]
- n_signatures (INTEGER): Number of signatures to identify [default: None] [required]
Options
- --lambd (FLOAT): Regularization parameter [default: 0.7]
- --opportunity-file (TEXT): The distribution of triplets in a reference 'human-genome' or 'human-exome' or normal tissue [default: None]
- --cosmic-file (TEXT): Tab separated cosmic file [default: None]
- --numeric-chromosomes: If set, chromosome names are numeric [default: no-numeric-chromosomes]
- --no-numeric-chromosomes: If set, chromosome names are not numeric [default: no-numeric-chromosomes]
- --genotyped: If set, VCF file has genotype information for many samples [default: genotyped]
- --no-genotyped: If set, VCF file does not have genotype information for many samples [default: genotyped]
- --max-em-iterations (INTEGER): Maximum EM iterations [default: 100]
- --max-gd-iterations (INTEGER): Maximum GD iterations [default: 50]
- --file-extension (TEXT): File extension [default: None]
- --ref-genome (TEXT): Path to the reference genome [default: None]
- --output-folder (TEXT): Path to the output folder [default: output/]
- --help: Show this message and exit
Step 1: Grid Search: The grid uses cross-validation to find the optimal pairwise (k and λ) by going to the snakemake folder and opening the running file (Snakefile) to check all the paths and input files:
cd snakemake vi Snakefile
Step 2: In the Snakefile, provide the range of the number of signatures k and λ for the grid search to determine the optimal k and λ:
localrules: all ks = list(range(2, 10)): default range of the number of signatures lambdas = [0, 0.01, 0.05, 0.1, 0.2]: default range of λ
Input mutational catalogue needs to be provided in the dataset folder:
rule test_train_split:
input: "results/{dataset}/M_catalogue.txt"
Running the grid search:
snakemake -j num_cpu
To check manually the optimal k and λ from the output:
sort -k3n,3 results/data/all.csv
Run denovo using optimal k=4 and λ=0.1:
starsigndna denovo snakemake/results/data/M_catalogue.txt 4 0.1 --cosmic-file example_data/COSMICv34.txt --output-folder /test_result
Maintainer Name - chrisbop@uio.no or christianbope@gmail.com