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Integrating omics datasets with the OmicsPLS package

Bouhaddani, Said El ; Uh, Hae-Won ; Jongbloed, Geurt ; Hayward, Caroline ; Klarić, Lucija ; Kiełbasa, Szymon M ; Houwing-Duistermaat, Jeanine

BMC bioinformatics, 2018-10, Vol.19 (1), p.371-371, Article 371 [Periódico revisado por pares]

England: BioMed Central Ltd

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Título:
Integrating omics datasets with the OmicsPLS package
Autor: Bouhaddani, Said El ; Uh, Hae-Won ; Jongbloed, Geurt ; Hayward, Caroline ; Klarić, Lucija ; Kiełbasa, Szymon M ; Houwing-Duistermaat, Jeanine
Assuntos: Analysis ; Bioinformatics ; Biomedical data ; Data integration ; Data integrity ; Data processing ; Data-specific variation ; Datasets ; Decomposition ; Economic models ; Freeware ; Genomics - methods ; Humans ; Information processing ; Integration ; Joint principal components ; Least-Squares Analysis ; Metabolomics - methods ; Methods ; O2PLS ; Omics data integration ; Open source software ; Principal components analysis ; R package ; Software ; Software packages ; Source code ; Variables
É parte de: BMC bioinformatics, 2018-10, Vol.19 (1), p.371-371, Article 371
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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Descrição: With the exponential growth in available biomedical data, there is a need for data integration methods that can extract information about relationships between the data sets. However, these data sets might have very different characteristics. For interpretable results, data-specific variation needs to be quantified. For this task, Two-way Orthogonal Partial Least Squares (O2PLS) has been proposed. To facilitate application and development of the methodology, free and open-source software is required. However, this is not the case with O2PLS. We introduce OmicsPLS, an open-source implementation of the O2PLS method in R. It can handle both low- and high-dimensional datasets efficiently. Generic methods for inspecting and visualizing results are implemented. Both a standard and faster alternative cross-validation methods are available to determine the number of components. A simulation study shows good performance of OmicsPLS compared to alternatives, in terms of accuracy and CPU runtime. We demonstrate OmicsPLS by integrating genetic and glycomic data. We propose the OmicsPLS R package: a free and open-source implementation of O2PLS for statistical data integration. OmicsPLS is available at https://cran.r-project.org/package=OmicsPLS and can be installed in R via install.packages("OmicsPLS").
Editor: England: BioMed Central Ltd
Idioma: Inglês

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Integrating omics datasets with the OmicsPLS package

Bouhaddani, Said El ; Uh, Hae-Won ; Jongbloed, Geurt ; Hayward, Caroline ; Klarić, Lucija ; Kiełbasa, Szymon M ; Houwing-Duistermaat, Jeanine

England: BioMed Central Ltd

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