Heterogeneous large datasets integration using Bayesian factor regression

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• dc.contributor.author Avalos-Pacheco, Alejandra
• dc.contributor.author Rossell Ribera, David
• dc.contributor.author Savage, Richard S.
• dc.date.accessioned 2023-07-28T06:56:08Z
• dc.date.available 2023-07-28T06:56:08Z
• dc.date.issued 2022
• dc.description.abstract Two key challenges in modern statistical applications are the large amount of information recorded per individual, and that such data are often not collected all at once but in batches. These batch effects can be complex, causing distortions in both mean and variance. We propose a novel sparse latent factor regression model to integrate such heterogeneous data. The model provides a tool for data exploration via dimensionality reduction and sparse low-rank covariance estimation while correcting for a range of batch effects. We study the use of several sparse priors (local and non-local) to learn the dimension of the latent factors. We provide a flexible methodology for sparse factor regression which is not limited to data with batch effects. Our model is fitted in a deterministic fashion by means of an EM algorithm for which we derive closed-form updates, contributing a novel scalable algorithm for non-local priors of interest beyond the immediate scope of this paper. We present several examples, with a focus on bioinformatics applications. Our results show an increase in the accuracy of the dimensionality reduction, with non-local priors substantially improving the reconstruction of factor cardinality. The results of our analyses illustrate how failing to properly account for batch effects can result in unreliable inference. Our model provides a novel approach to latent factor regression that balances sparsity with sensitivity in scenarios both with and without batch effects and is highly computationally efficient.
• dc.description.sponsorship Partially funded by the NIH grant R01 CA158113-01, RyC-2015-18544, Ayudas Fundación BBVA a equipos de investigación científica 2017 and Spanish Plan Estatal grant PGC2018-101643-B-I00.
• dc.format.mimetype application/pdf
• dc.identifier.citation Avalos-Pacheco A, Rossell D, Savage RS. Heterogeneous large datasets integration using Bayesian factor regression. Bayesian Anal. 2022;17(1):33-66. DOI: 10.1214/20-BA1240
• dc.identifier.doi http://dx.doi.org/10.1214/20-BA1240
• dc.identifier.issn 1936-0975
• dc.identifier.uri http://hdl.handle.net/10230/57702
• dc.language.iso eng
• dc.publisher International Society for Bayesian Analysis
• dc.relation.ispartof Bayesian Analysis. 2022;17(1):33-66.
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PGC2018-101643-B-I00
• dc.rights © 2022 International Society for Bayesian Analysis. This publication is under a Creative Commons Attribution 4.0 International License.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Bayesian factor analysis
• dc.subject.keyword EM
• dc.subject.keyword non-local priors
• dc.subject.keyword shrinkage
• dc.title Heterogeneous large datasets integration using Bayesian factor regression
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion