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SparseSignatures: An R package using LASSO-regularized non-negative matrix factorization to identify mutational
Lorenzo Mella1, Avantika Lal2, Fabrizio Angaroni3
1Department of Biomedical Sciences, Humanitas University, Rozzano, Italy.
STAR Protocols
|July 2, 2022
Summary
The R package SparseSignatures aids in identifying cancer mutation signatures. This study details its use for analyzing tumor mutation profiles and determining exposure levels.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Tumor mutation profiles provide insights into cancer development.
- Identifying the underlying mutational signatures is crucial for understanding cancer etiology.
- Computational tools are needed to analyze complex genomic data.
Purpose of the Study:
- To introduce the R package SparseSignatures for mutation signature analysis.
- To provide a step-by-step guide for applying SparseSignatures to tumor mutation data.
- To demonstrate the determination of mutational signatures and exposure levels.
Main Methods:
- Utilizing the SparseSignatures R package.
- Data preparation for signature analysis.
- Parameter optimization for signature discovery.
- Application to point mutation datasets.
Main Results:
- Successful application of SparseSignatures to identify mutation signatures.
- Determination of optimal parameters for analysis.
- Estimation of signature contributions and related exposure levels.
Conclusions:
- SparseSignatures is a valuable tool for cancer mutation signature analysis.
- The package facilitates a systematic approach to uncovering mutational processes.
- This method aids in understanding the etiological factors of cancer.
