MOSS: multi-omic integration with sparse value decomposition

SUMMARY: This article presents multi-omic integration with sparse value decomposition (MOSS), a free and open-source R package for integration and feature selection in multiple large omics datasets. This package is computationally efficient and offers biological insight through capabilities, such as...

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Detalles Bibliográficos
Autores principales: Gonzalez-Reymundez, Agustin, Grueneberg, Alexander, Lu, Guanqi, Alves, Filipe Couto, Rincon, Gonzalo, Vazquez, Ana I
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Applications Notes
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9113319/
https://www.ncbi.nlm.nih.gov/pubmed/35561193
http://dx.doi.org/10.1093/bioinformatics/btac179
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author Gonzalez-Reymundez, Agustin
Grueneberg, Alexander
Lu, Guanqi
Alves, Filipe Couto
Rincon, Gonzalo
Vazquez, Ana I
author_facet Gonzalez-Reymundez, Agustin
Grueneberg, Alexander
Lu, Guanqi
Alves, Filipe Couto
Rincon, Gonzalo
Vazquez, Ana I
author_sort Gonzalez-Reymundez, Agustin
collection PubMed
description SUMMARY: This article presents multi-omic integration with sparse value decomposition (MOSS), a free and open-source R package for integration and feature selection in multiple large omics datasets. This package is computationally efficient and offers biological insight through capabilities, such as cluster analysis and identification of informative omic features. AVAILABILITY AND IMPLEMENTATION: https://CRAN.R-project.org/package=MOSS. SUPPLEMENTARY INFORMATION: Supplementary information can be found at https://github.com/agugonrey/GonzalezReymundez2021.
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spelling pubmed-91133192022-05-18 MOSS: multi-omic integration with sparse value decomposition Gonzalez-Reymundez, Agustin Grueneberg, Alexander Lu, Guanqi Alves, Filipe Couto Rincon, Gonzalo Vazquez, Ana I Bioinformatics Applications Notes SUMMARY: This article presents multi-omic integration with sparse value decomposition (MOSS), a free and open-source R package for integration and feature selection in multiple large omics datasets. This package is computationally efficient and offers biological insight through capabilities, such as cluster analysis and identification of informative omic features. AVAILABILITY AND IMPLEMENTATION: https://CRAN.R-project.org/package=MOSS. SUPPLEMENTARY INFORMATION: Supplementary information can be found at https://github.com/agugonrey/GonzalezReymundez2021. Oxford University Press 2022-03-24 /pmc/articles/PMC9113319/ /pubmed/35561193 http://dx.doi.org/10.1093/bioinformatics/btac179 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Notes
Gonzalez-Reymundez, Agustin
Grueneberg, Alexander
Lu, Guanqi
Alves, Filipe Couto
Rincon, Gonzalo
Vazquez, Ana I
MOSS: multi-omic integration with sparse value decomposition
title MOSS: multi-omic integration with sparse value decomposition
title_full MOSS: multi-omic integration with sparse value decomposition
title_fullStr MOSS: multi-omic integration with sparse value decomposition
title_full_unstemmed MOSS: multi-omic integration with sparse value decomposition
title_short MOSS: multi-omic integration with sparse value decomposition
title_sort moss: multi-omic integration with sparse value decomposition
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9113319/
https://www.ncbi.nlm.nih.gov/pubmed/35561193
http://dx.doi.org/10.1093/bioinformatics/btac179
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AT alvesfilipecouto mossmultiomicintegrationwithsparsevaluedecomposition
AT rincongonzalo mossmultiomicintegrationwithsparsevaluedecomposition
AT vazquezanai mossmultiomicintegrationwithsparsevaluedecomposition

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