Random Effects Model for Multiple Pathway Analysis with Applications to Type II Diabetes Microarray Data.

TitleRandom Effects Model for Multiple Pathway Analysis with Applications to Type II Diabetes Microarray Data.
Publication TypeJournal Article
Year of Publication2015
AuthorsPang, Herbert, Inyoung Kim, and Hongyu Zhao
JournalStat Biosci
Date Published2015 Oct 01

Close to three percent of the world's population suffer from diabetes. Despite the range of treatment options available for diabetes patients, not all patients benefit from them. Investigating how different pathways correlate with phenotype of interest may help unravel novel drug targets and discover a possible cure. Many pathway-based methods have been developed to incorporate biological knowledge into the study of microarray data. Most of these methods can only analyze individual pathways but cannot deal with two or more pathways in a model based framework. This represents a serious limitation because, like genes, individual pathways do not work in isolation, and joint modeling may enable researchers to uncover patterns not seen in individual pathway-based analysis. In this paper, we propose a random effects model to analyze two or more pathways. We also derive score test statistics for significance of pathway effects. We apply our method to a microarray study of Type II diabetes. Our method may eludicate how pathways crosstalk with each other and facilitate the investigation of pathway crosstalks. Further hypothesis on the biological mechanisms underlying the disease and traits of interest may be generated and tested based on this method.

Alternate JournalStat Biosci
Original PublicationRandom effects model for multiple pathway analysis with applications to type II diabetes microarray data.
PubMed ID26640601
PubMed Central IDPMC4666561
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
P01 CA154295 / CA / NCI NIH HHS / United States
R01 GM059507 / GM / NIGMS NIH HHS / United States
S10 RR019895 / RR / NCRR NIH HHS / United States