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

• Published in: BMC bioinformatics Vol. 13; no. 1; p. 94
• Main Authors: Hill, Steven M, Neve, Richard M, Bayani, Nora, Kuo, Wen-Lin, Ziyad, Safiyyah, Spellman, Paul T, Gray, Joe W, Mukherjee, Sach
• Format: Journal Article
• Language: English
• Published: London BioMed Central 11.05.2012; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Antineoplastic Agents - pharmacology; BASIC BIOLOGICAL SCIENCES; Bayes Theorem; Bayesian analysis; Biochemistry & Molecular Biology; Bioinformatics; Biology; Biomarkers, Pharmacological - metabolism; Biomedical and Life Sciences; Biotechnology & Applied Microbiology; Cancer; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Computer Simulation; Data processing; Gene expression; Health aspects; Humans; Life Sciences; Likelihood Functions; Markov processes; Mathematical & Computational Biology; Mathematical models; MATHEMATICS AND COMPUTING; Medical research; Methodology; Methodology Article; Methods; Microarrays; Models, Biological; Neoplasms - metabolism; Networks analysis; Probability; Research Design; Science; Statistics; Studies; United Kingdom; Taiwan; Inclusion Probability; Markov Random Field; Bayesian Variable Selection; Marginal Likelihood; Lasso
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-13-94

Background 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. Results 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 and frequentist penalised-likelihood methods for incorporating network-based information. Conclusions The empirical Bayes method proposed here can aid prior elicitation for Bayesian variable selection studies and help to guard against mis-specification of priors. Empirical Bayes, together with the proposed pathway-based priors, results in an approach with a competitive variable selection performance. In addition, the overall procedure is fast, deterministic, and has very few user-set parameters, yet is capable of capturing interplay between molecular players. The approach presented is general and readily applicable in any setting with multiple sources of biological prior knowledge.