Ontology type: schema:ScholarlyArticle Open Access: True
2019-12
AUTHORSFang Shao, Yaqi Wang, Yang Zhao, Sheng Yang
ABSTRACTBACKGROUND: RNA sequencing (RNA-seq) technology has identified multiple differentially expressed (DE) genes associated to complex disease, however, these genes only explain a modest part of variance. Omnigenic model assumes that disease may be driven by genes with indirect relevance to disease and be propagated by functional pathways. Here, we focus on identifying the interactions between the external genes and functional pathways, referring to gene-pathway interactions (GPIs). Specifically, relying on the relationship between the garrote kernel machine (GKM) and variance component test and permutations for the empirical distributions of score statistics, we propose an efficient analysis procedure as Permutation based gEne-pAthway interaction identification in binary phenotype (PEA). RESULTS: Various simulations show that PEA has well-calibrated type I error rates and higher power than the traditional likelihood ratio test (LRT). In addition, we perform the gene set enrichment algorithms and PEA to identifying the GPIs from a pan-cancer data (GES68086). These GPIs and genes possibly further illustrate the potential etiology of cancers, most of which are identified and some external genes and significant pathways are consistent with previous studies. CONCLUSIONS: PEA is an efficient tool for identifying the GPIs from RNA-seq data. It can be further extended to identify the interactions between one variable and one functional set of other omics data for binary phenotypes. More... »
PAGES36
http://scigraph.springernature.com/pub.10.1186/s12863-019-0739-7
DOIhttp://dx.doi.org/10.1186/s12863-019-0739-7
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PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/30890140
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