A whitening approach to probabilistic canonical correlation analysis for omics data integration

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Title: A whitening approach to probabilistic canonical correlation analysis for omics data integration
Authors: Jendoubi Bedhiafi, T
Strimmer, K
Item Type: Journal Article
Abstract: ackground Canonical correlation analysis (CCA) is a classic statistical tool for investigating complex multivariate data. Correspondingly, it has found many diverse applications, ranging from molecular biology and medicine to social science and finance. Intriguingly, despite the importance and pervasiveness of CCA, only recently a probabilistic understanding of CCA is developing, moving from an algorithmic to a model-based perspective and enabling its application to large-scale settings. Results Here, we revisit CCA from the perspective of statistical whitening of random variables and propose a simple yet flexible probabilistic model for CCA in the form of a two-layer latent variable generative model. The advantages of this variant of probabilistic CCA include non-ambiguity of the latent variables, provisions for negative canonical correlations, possibility of non-normal generative variables, as well as ease of interpretation on all levels of the model. In addition, we show that it lends itself to computationally efficient estimation in high-dimensional settings using regularized inference. We test our approach to CCA analysis in simulations and apply it to two omics data sets illustrating the integration of gene expression data, lipid concentrations and methylation levels. Conclusions Our whitening approach to CCA provides a unifying perspective on CCA, linking together sphering procedures, multivariate regression and corresponding probabilistic generative models. Furthermore, we offer an efficient computer implementation in the “whitening” R package available at https://CRAN.R-project.org/package=whitening.
Issue Date: 9-Jan-2019
Date of Acceptance: 5-Dec-2018
URI: http://hdl.handle.net/10044/1/66669
DOI: https://dx.doi.org/10.1186/s12859-018-2572-9
ISSN: 1471-2105
Publisher: BioMed Central
Journal / Book Title: BMC Bioinformatics
Volume: 20
Copyright Statement: © The Author(s). 2018Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to theCreative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Keywords: Science & Technology
Life Sciences & Biomedicine
Biochemical Research Methods
Biotechnology & Applied Microbiology
Mathematical & Computational Biology
Biochemistry & Molecular Biology
Multivariate analysis
Probabilistic canonical correlation analysis
Data integration
Data integration
Multivariate analysis
Probabilistic canonical correlation analysis
06 Biological Sciences
08 Information And Computing Sciences
01 Mathematical Sciences
Publication Status: Published
Article Number: ARTN 15
Appears in Collections:Mathematics
Faculty of Natural Sciences

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