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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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
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author Mella, Lorenzo
Lal, Avantika
Angaroni, Fabrizio
Maspero, Davide
Piazza, Rocco
Sidow, Arend
Antoniotti, Marco
Graudenzi, Alex
Ramazzotti, Daniele
author_facet Mella, Lorenzo
Lal, Avantika
Angaroni, Fabrizio
Maspero, Davide
Piazza, Rocco
Sidow, Arend
Antoniotti, Marco
Graudenzi, Alex
Ramazzotti, Daniele
author_sort Mella, Lorenzo
collection PubMed
description 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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spelling pubmed-92568272022-07-07 SparseSignatures: An R package using LASSO-regularized non-negative matrix factorization to identify mutational signatures from human tumor samples Mella, Lorenzo Lal, Avantika Angaroni, Fabrizio Maspero, Davide Piazza, Rocco Sidow, Arend Antoniotti, Marco Graudenzi, Alex Ramazzotti, Daniele STAR Protoc Protocol 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). Elsevier 2022-07-01 /pmc/articles/PMC9256827/ /pubmed/35779264 http://dx.doi.org/10.1016/j.xpro.2022.101513 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Protocol
Mella, Lorenzo
Lal, Avantika
Angaroni, Fabrizio
Maspero, Davide
Piazza, Rocco
Sidow, Arend
Antoniotti, Marco
Graudenzi, Alex
Ramazzotti, Daniele
SparseSignatures: An R package using LASSO-regularized non-negative matrix factorization to identify mutational signatures from human tumor samples
title SparseSignatures: An R package using LASSO-regularized non-negative matrix factorization to identify mutational signatures from human tumor samples
title_full SparseSignatures: An R package using LASSO-regularized non-negative matrix factorization to identify mutational signatures from human tumor samples
title_fullStr SparseSignatures: An R package using LASSO-regularized non-negative matrix factorization to identify mutational signatures from human tumor samples
title_full_unstemmed SparseSignatures: An R package using LASSO-regularized non-negative matrix factorization to identify mutational signatures from human tumor samples
title_short SparseSignatures: An R package using LASSO-regularized non-negative matrix factorization to identify mutational signatures from human tumor samples
title_sort sparsesignatures: an r package using lasso-regularized non-negative matrix factorization to identify mutational signatures from human tumor samples
topic Protocol
url 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
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