Pathway crosstalk effects: shrinkage and disentanglement using a Bayesian hierarchical model

Alin Tomoiaga (), Peter Westfall (), Michele Donato, Sorin Draghici, Sonia Hassan, Roberto Romero and Paola Tellaroli
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Alin Tomoiaga: Texas Tech University
Peter Westfall: Texas Tech University
Michele Donato: Wayne State University School of Medicine
Sorin Draghici: Wayne State University School of Medicine
Sonia Hassan: Eunice Kennedy Shriver National Institute of Child Health and Human Development
Roberto Romero: Eunice Kennedy Shriver National Institute of Child Health and Human Development
Paola Tellaroli: University of Padua

Statistics in Biosciences, 2016, vol. 8, issue 2, No 12, 374-394

Abstract: Abstract Identifying the biological pathways that are related to various clinical phenotypes is an important concern in biomedical research. Based on estimated expression levels and/or p values, overrepresentation analysis (ORA) methods provide rankings of pathways, but they are tainted because pathways overlap. This crosstalk phenomenon has not been rigorously studied and classical ORA does not take into consideration: (1) that crosstalk effects in cases of overlapping pathways can cause incorrect rankings of pathways, (2) that crosstalk effects can cause both excess type I errors and type II errors, (3) that rankings of small pathways are unreliable, and (4) that type I error rates can be inflated due to multiple comparisons of pathways. We develop a Bayesian hierarchical model that addresses these problems, providing sensible estimates and rankings, and reducing error rates. We show, on both real and simulated data, that the results of our method are more accurate than the results produced by the classical overrepresentation analysis, providing a better understanding of the underlying biological phenomena involved in the phenotypes under study. The R code and the binary datasets for implementing the analyses described in this article are available online at: http://www.eng.wayne.edu/page.php?id=6402 .

Keywords: Bayes model; hierarchical modeling; data augmentation; genomic pathway analysis; gene expression (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s12561-016-9160-1

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