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March 2015 A multi-functional analyzer uses parameter constraints to improve the efficiency of model-based gene-set analysis
Zhishi Wang, Qiuling He, Bret Larget, Michael A. Newton
Ann. Appl. Stat. 9(1): 225-246 (March 2015). DOI: 10.1214/14-AOAS777

Abstract

We develop a model-based methodology for integrating gene-set information with an experimentally-derived gene list. The methodology uses a previously reported sampling model, but takes advantage of natural constraints in the high-dimensional discrete parameter space in order to work from a more structured prior distribution than is currently available. We show how the natural constraints are expressed in terms of linear inequality constraints within a set of binary latent variables. Further, the currently available prior gives low probability to these constraints in complex systems, such as Gene Ontology (GO), thus reducing the efficiency of statistical inference. We develop two computational advances to enable posterior inference within the constrained parameter space: one using integer linear programming for optimization and one using a penalized Markov chain sampler. Numerical experiments demonstrate the utility of the new methodology for a multivariate integration of genomic data with GO or related information systems. Compared to available methods, the proposed multi-functional analyzer covers more reported genes without mis-covering nonreported genes, as demonstrated on genome-wide data from association studies of type 2 diabetes and from RNA interference studies of influenza.

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Zhishi Wang. Qiuling He. Bret Larget. Michael A. Newton. "A multi-functional analyzer uses parameter constraints to improve the efficiency of model-based gene-set analysis." Ann. Appl. Stat. 9 (1) 225 - 246, March 2015. https://doi.org/10.1214/14-AOAS777

Information

Published: March 2015
First available in Project Euclid: 28 April 2015

zbMATH: 06446567
MathSciNet: MR3341114
Digital Object Identifier: 10.1214/14-AOAS777

Keywords: Bayesian analysis , Gene-set enrichment , integer linear programming

Rights: Copyright © 2015 Institute of Mathematical Statistics

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Vol.9 • No. 1 • March 2015
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