BayesSUR: An R Package for High-Dimensional Multivariate Bayesian Variable and Covariance Selection in Linear Regression
Dublin Core
Title
BayesSUR: An R Package for High-Dimensional Multivariate Bayesian Variable and Covariance Selection in Linear Regression
Subject
: seemingly unrelated regression, Bayesian multivariate regression, structured covariance matrix, Markov random field prior, multi-omics data.
Description
In molecular biology, advances in high-throughput technologies have made it possible to study complex multivariate phenotypes and their simultaneous associations with
high-dimensional genomic and other omics data, a problem that can be studied with
high-dimensional multi-response regression, where the response variables are potentially
highly correlated. To this purpose, we recently introduced several multivariate Bayesian
variable and covariance selection models, e.g., Bayesian estimation methods for sparse
seemingly unrelated regression for variable and covariance selection. Several variable selection priors have been implemented in this context, in particular the hotspot detection
prior for latent variable inclusion indicators, which results in sparse variable selection for
associations between predictors and multiple phenotypes. We also propose an alternative,
which uses a Markov random field (MRF) prior for incorporating prior knowledge about
the dependence structure of the inclusion indicators. Inference of Bayesian seemingly unrelated regression (SUR) by Markov chain Monte Carlo methods is made computationally
feasible by factorization of the covariance matrix amongst the response variables.
In this paper we present BayesSUR, an R package, which allows the user to easily
specify and run a range of different Bayesian SUR models, which have been implemented in
C++ for computational efficiency. The R package allows the specification of the models in
a modular way, where the user chooses the priors for variable selection and for covariance
selection separately. We demonstrate the performance of sparse SUR models with the
hotspot prior and spike-and-slab MRF prior on synthetic and real data sets representing
eQTL or mQTL studies and in vitro anti-cancer drug screening studies as examples for
typical applications.
high-dimensional genomic and other omics data, a problem that can be studied with
high-dimensional multi-response regression, where the response variables are potentially
highly correlated. To this purpose, we recently introduced several multivariate Bayesian
variable and covariance selection models, e.g., Bayesian estimation methods for sparse
seemingly unrelated regression for variable and covariance selection. Several variable selection priors have been implemented in this context, in particular the hotspot detection
prior for latent variable inclusion indicators, which results in sparse variable selection for
associations between predictors and multiple phenotypes. We also propose an alternative,
which uses a Markov random field (MRF) prior for incorporating prior knowledge about
the dependence structure of the inclusion indicators. Inference of Bayesian seemingly unrelated regression (SUR) by Markov chain Monte Carlo methods is made computationally
feasible by factorization of the covariance matrix amongst the response variables.
In this paper we present BayesSUR, an R package, which allows the user to easily
specify and run a range of different Bayesian SUR models, which have been implemented in
C++ for computational efficiency. The R package allows the specification of the models in
a modular way, where the user chooses the priors for variable selection and for covariance
selection separately. We demonstrate the performance of sparse SUR models with the
hotspot prior and spike-and-slab MRF prior on synthetic and real data sets representing
eQTL or mQTL studies and in vitro anti-cancer drug screening studies as examples for
typical applications.
Creator
Zhi Zhao
Source
https://www.jstatsoft.org/article/view/v100i11
Publisher
University of Oslo
Date
November 2021
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Zhi Zhao
, “BayesSUR: An R Package for High-Dimensional Multivariate Bayesian Variable and Covariance Selection in Linear Regression,” Repository Horizon University Indonesia, accessed April 28, 2025, https://repository.horizon.ac.id/items/show/8224.