SparseSignatures: An R package using LASSO-regularized non-negative matrix factorization to identify mutational signatures from human tumor samples

Lorenzo Mella; Avantika Lal; Fabrizio Angaroni; Davide Maspero; Rocco Piazza; Arend Sidow; Marco Antoniotti; Alex Graudenzi; Daniele Ramazzotti

doi:10.1016/j.xpro.2022.101513

SparseSignatures: An R package using LASSO-regularized non-negative matrix factorization to identify mutational signatures from human tumor samples

STAR Protoc. 2022 Sep 16;3(3):101513. doi: 10.1016/j.xpro.2022.101513. Epub 2022 Jul 1.

Authors

Lorenzo Mella¹, Avantika Lal², Fabrizio Angaroni³, Davide Maspero³, Rocco Piazza⁴, Arend Sidow⁵, Marco Antoniotti⁶, Alex Graudenzi⁷, Daniele Ramazzotti⁸

Affiliations

¹ Department of Biomedical Sciences, Humanitas University, Rozzano, Italy.
² Insitro, 279 E Grand Avenue, South San Francisco, CA, USA.
³ Department of Informatics, Systems and Communication, Università degli Studi di Milano-Bicocca, Milan, Italy.
⁴ Department of Medicine and Surgery, Università degli Studi di Milano-Bicocca, Milan, Italy; Bicocca Bioinformatics Biostatistics and Bioimaging Centre - B4, Milan, Italy.
⁵ Department of Pathology, Stanford University, Stanford, CA, USA.
⁶ Department of Informatics, Systems and Communication, Università degli Studi di Milano-Bicocca, Milan, Italy; Bicocca Bioinformatics Biostatistics and Bioimaging Centre - B4, Milan, Italy. Electronic address: [email protected].
⁷ Department of Informatics, Systems and Communication, Università degli Studi di Milano-Bicocca, Milan, Italy; Bicocca Bioinformatics Biostatistics and Bioimaging Centre - B4, Milan, Italy; Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Segrate, Italy. Electronic address: [email protected].
⁸ Department of Medicine and Surgery, Università degli Studi di Milano-Bicocca, Milan, Italy. Electronic address: [email protected].

Abstract

We outline the features of the R package SparseSignatures and its application to determine the signatures contributing to mutation profiles of tumor samples. We describe installation details and illustrate a step-by-step approach to (1) prepare the data for signature analysis, (2) determine the optimal parameters, and (3) employ them to determine the signatures and related exposure levels in the point mutation dataset. For complete details on the use and execution of this protocol, please refer to Lal et al. (2021).

Keywords: Bioinformatics; Cancer; Genomics.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Humans
Mutation
Neoplasms* / diagnosis