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

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 opti...

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Detalles Bibliográficos
Autores principales: Mella, Lorenzo, Lal, Avantika, Angaroni, Fabrizio, Maspero, Davide, Piazza, Rocco, Sidow, Arend, Antoniotti, Marco, Graudenzi, Alex, Ramazzotti, Daniele
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Protocol
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9256827/
https://www.ncbi.nlm.nih.gov/pubmed/35779264
http://dx.doi.org/10.1016/j.xpro.2022.101513
Descripción
Sumario: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).

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