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BMC Bioinformatics

, 18:336

Networks analysis

Abstract

BackgroundBurgeoning interest in integrative analyses has produced a rise in studies which incorporate data from multiple genomic platforms. Literature for conducting formal hypothesis testing on an integrative gene set level is considerably sparse. This paper is biologically motivated by our interest in the joint effects of epigenetic methylation loci and their associated mRNA gene expressions on lung cancer survival status.

ResultsWe provide an efficient screening approach across multiplatform genomic data on the level of biologically related sets of genes, and our methods are applicable to various disease models regardless whether the underlying true model is known iTEGS or unknown iNOTE. Our proposed testing procedure dominated two competing methods. Using our methods, we identified a total of 28 gene sets with significant joint epigenomic and transcriptomic effects on one-year lung cancer survival.

ConclusionsWe propose efficient variance component-based testing procedures to facilitate the joint testing of multiplatform genomic data across an entire gene set. The testing procedure for the gene set is self-contained, and can easily be extended to include more or different genetic platforms. iTEGS and iNOTE implemented in R are freely available through the inote package at https:-cran.r-project.org-.

KeywordsPathway analysis Data integration Epigenetics Gene expression Gene set analysis Integrative genomics AbbreviationsDNAmDNA methylation

iTEGSintegrated total effect of a gene set

iNOTEIntegrated network omnibus total effect test

GSAAGene set association analysis

MRisk model with only methylation effects

GRisk model with only gene expression effects

MGRisk model with only methylation and gene expression main effects

MGCRisk model for methylation, gene expression, and their interactions

Electronic supplementary materialThe online version of this article doi:10.1186-s12859-017-1737-2 contains supplementary material, which is available to authorized users.

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Author: Su Hee Chu - Yen-Tsung Huang

Source: https://link.springer.com/







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