Integrating biological knowledge into variable selection: an empirical Bayes approach with an application in cancer biology.

BMC Bioinformatics
Steven M HillSach Mukherjee

Abstract

An important question in the analysis of biochemical data is that of identifying subsets of molecular variables that may jointly influence a biological response. Statistical variable selection methods have been widely used for this purpose. In many settings, it may be important to incorporate ancillary biological information concerning the variables of interest. Pathway and network maps are one example of a source of such information. However, although ancillary information is increasingly available, it is not always clear how it should be used nor how it should be weighted in relation to primary data. We put forward an approach in which biological knowledge is incorporated using informative prior distributions over variable subsets, with prior information selected and weighted in an automated, objective manner using an empirical Bayes formulation. We employ continuous, linear models with interaction terms and exploit biochemically-motivated sparsity constraints to permit exact inference. We show an example of priors for pathway- and network-based information and illustrate our proposed method on both synthetic response data and by an application to cancer drug response data. Comparisons are also made to alternative Bayesian an...Continue Reading

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Citations

Jul 21, 2012·Bioinformatics·Chris J OatesSach Mukherjee
Aug 28, 2012·Bioinformatics·Steven M HillSach Mukherjee
Jan 22, 2013·Expert Opinion on Medical Diagnostics·Jason E McDermottKarin D Rodland
Oct 31, 2015·Statistics in Medicine·Christine B PetersonMarina Vannucci
Oct 26, 2012·Journal of Animal Science·W M SnellingA Reverter
Oct 11, 2017·Current Environmental Health Reports·Massimo StafoggiaXavier Basagaña
Aug 20, 2019·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Yang-Fan ZouXin Yu
Apr 21, 2018·Science Advances·Meenakshi Chatterjee, Murat Acar
Dec 31, 2016·Evolutionary Applications·Li LiuJieping Ye

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Software Mentioned

Matlab package glmnet
MATLAB

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