Bayesian Methods for Gene Expression Analysis

Lewin, Arie Y.; Bottolo, Leonardo; Richardson, Sylvia

doi:10.1002/9781119487845.ch30

Cited by 2 publications

(1 citation statement)

References 154 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, the LASSO, ridge and elastic-net penalties have all been extended to the PHM ( Gui and Li, 2005 ; Simon et al , 2011 ; Tibshirani, 1997 ; Zou and Hastie, 2005 ). More recently, Bayesian shrinkage and variable selection methods have grown in popularity ( Bai et al , 2021 ; Bhadra et al , 2019 ; Carvalho et al , 2010 ; Lewin et al , 2019 ; Li and Zhang, 2010 ; O’Hara and Sillanpää, 2009 ; Park and Casella, 2008 ), with several methods being extended to survival data ( Maity et al , 2020 ; Nikooienejad et al , 2020 ; Tang et al , 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Variational Bayes for high-dimensional proportional hazards models with applications within gene expression

et al. 2022

View full text Add to dashboard Cite

Motivation Few Bayesian methods for analyzing high-dimensional sparse survival data provide scalable variable selection, effect estimation and uncertainty quantification. Such methods often either sacrifice uncertainty quantification by computing maximum a posteriori estimates, or quantify the uncertainty at high (unscalable) computational expense. Results We bridge this gap and develop an interpretable and scalable Bayesian proportional hazards model for prediction and variable selection, referred to as SVB. Our method, based on a mean-field variational approximation, overcomes the high computational cost of MCMC whilst retaining useful features, providing a posterior distribution for the parameters and offering a natural mechanism for variable selection via posterior inclusion probabilities. The performance of our proposed method is assessed via extensive simulations and compared against other state-of-the-art Bayesian variable selection methods, demonstrating comparable or better performance. Finally, we demonstrate how the proposed method can be used for variable selection on two transcriptomic datasets with censored survival outcomes, and how the uncertainty quantification offered by our method can be used to provide an interpretable assessment of patient risk. Availability and implementation our method has been implemented as a freely available R package survival.svb (https://github.com/mkomod/survival.svb). Supplementary information Supplementary materials are available at Bioinformatics online.

show abstract