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Statistics in Biosciences

, Volume 8, Issue 2, pp 374–394 | Cite as

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

  • Alin Tomoiaga
  • Peter Westfall
  • Michele Donato
  • Sorin Draghici
  • Sonia Hassan
  • Roberto Romero
  • Paola Tellaroli
Article
  • 121 Downloads

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 

Notes

Acknowledgments

This work has been partially supported by the following Grants: NIH RO1 RDK089167, R42 GM087013, and NSF DBI-0965741 (to S.D.), by PARO112419, by the Robert J. Sokol Endowment in Systems Biology, by the Wayne State University Perinatal Initiative, and by the Perinatology Research Branch, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, DHHS. The authors gratefully acknowledge the comments of a reviewer to improve this manuscript. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF, NIH, or any other of the funding agencies.

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Copyright information

© International Chinese Statistical Association 2016

Authors and Affiliations

  • Alin Tomoiaga
    • 1
  • Peter Westfall
    • 1
  • Michele Donato
    • 2
  • Sorin Draghici
    • 2
  • Sonia Hassan
    • 3
  • Roberto Romero
    • 3
  • Paola Tellaroli
    • 4
  1. 1.Center for Advanced Analytics and Business IntelligenceTexas Tech UniversityLubbockUSA
  2. 2.Department of Obstetrics and GynecologyWayne State University School of MedicineDetroitUSA
  3. 3.Perinatology Research Branch, Program for Perinatal Research and Obstetrics, Division of Intramural ResearchEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNIH DetroitUSA
  4. 4.Department of Statistical SciencesUniversity of PaduaPaduaItaly

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