The Library
Integrating biological knowledge into variable selection : an empirical Bayes approach with an application in cancer biology
Tools
Hill, Steven M. (Mark), Neve, Richard M., Bayani, Nora, Kuo, Wen-Lin, Ziyad, Safiyyah, Spellman, Paul T., Gray, Joe W. and Mukherjee, Sach (2012) Integrating biological knowledge into variable selection : an empirical Bayes approach with an application in cancer biology. BMC Bioinformatics, Volume 13 (Number 1). Article no. 94. doi:10.1186/1471-2105-13-94 ISSN 1471-2105.
|
Text
WRAP_Hill_1471-2105-13-94.pdf - Published Version Available under License Creative Commons Attribution 2.0.. Download (1446Kb) | Preview |
Official URL: http://dx.doi.org/10.1186/1471-2105-13-94
Abstract
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.
Item Type: | Journal Article | ||||
---|---|---|---|---|---|
Subjects: | Q Science > QA Mathematics Q Science > QD Chemistry Q Science > QR Microbiology |
||||
Divisions: | Faculty of Science, Engineering and Medicine > Research Centres > Centre for Complexity Science Faculty of Science, Engineering and Medicine > Science > Statistics |
||||
Library of Congress Subject Headings (LCSH): | Biology -- Mathematical models, Biomathematics, Bayesian statistical decision theory , Oncology -- Research, Biochemistry -- Data processing, Molecules -- Models | ||||
Journal or Publication Title: | BMC Bioinformatics | ||||
Publisher: | BioMed Central Ltd. | ||||
ISSN: | 1471-2105 | ||||
Official Date: | 11 May 2012 | ||||
Dates: |
|
||||
Volume: | Volume 13 | ||||
Number: | Number 1 | ||||
Page Range: | Article no. 94 | ||||
DOI: | 10.1186/1471-2105-13-94 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Open Access (Creative Commons) | ||||
Date of first compliant deposit: | 24 December 2015 | ||||
Date of first compliant Open Access: | 24 December 2015 | ||||
Funder: | United States. Dept. of Energy. Office of Basic Energy Sciences (OBES), National Institutes of Health (U.S.) (NIH), National Cancer Institute (U.S.) (NCI), Nederlandse Organisatie voor Wetenschappelijk Onderzoek [Netherlands Organisation for Scientific Research] (NWO), Engineering and Physical Sciences Research Council (EPSRC) | ||||
Grant number: | DE-AC02-05CH11231 (OBES) ; U54 CA 112970, P50 CA 58207(NCI) ; EP/E501311/1 (EPSRC) |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year