Developing variational Bayesian inference for applications to gene expression data Walker, David
dc.contributor.department Statistics
dc.contributor.majorProfessor Peng Liu 2021-09-09T16:08:51.000 2021-09-09T16:54:33Z 2021-09-09T16:54:33Z Fri Jan 01 00:00:00 UTC 2021 2021-07-16 2021-01-01
dc.description.abstract <p>Bayesian hierarchical generalized linear models are intuitively appealing for applications to gene sequencing data. However, they can be computationally costly to fit in high-dimensional settings using standard Markov-chain Monte Carlo methods. Here we explore the use of variational inference techniques to approximate the posterior of Bayesian hierarchical GLMMs for detecting differential expression in RNA-Seq data or differential translational efficiency in Ribo-Seq data. We find that in simulation studies the variational approach is comparable to two common methods for detecting differential expression, and that the variational posterior is close to the Markov-chain Monte Carlo posterior.</p>
dc.format.mimetype PDF
dc.identifier archive/
dc.identifier.articleid 1933
dc.identifier.contextkey 23871469
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath creativecomponents/895
dc.source.bitstream archive/|||Sat Jan 15 02:19:27 UTC 2022
dc.subject.disciplines Biostatistics
dc.subject.keywords Gene expression
dc.subject.keywords Bayesian statistics
dc.subject.keywords Variational Bayes
dc.subject.keywords RNA-Seq
dc.subject.keywords ribosome profiling
dc.title Developing variational Bayesian inference for applications to gene expression data
dc.type article
dc.type.genre creativecomponent
dspace.entity.type Publication
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca Statistics creativecomponent
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