MOSS: multi-omic integration with sparse value decomposition.

Autor:	Gonzalez-Reymundez A; Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, USA., Grueneberg A; Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, USA., Lu G; Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, USA., Alves FC; Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, USA., Rincon G; Genus PLC Inc., Genome Sciences R&D, De Forest, WI 53532, USA., Vazquez AI; Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, USA.
Jazyk:	angličtina
Zdroj:	Bioinformatics (Oxford, England) [Bioinformatics] 2022 May 13; Vol. 38 (10), pp. 2956-2958.
DOI:	10.1093/bioinformatics/btac179
Abstrakt:	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. (© The Author(s) 2022. Published by Oxford University Press.)
Databáze:	MEDLINE
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