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Identifying and exploiting gene-pathway interactions from RNA-seq data for binary phenotype

机译：从RNA-seq数据中识别和利用二元表型的基因途径相互作用

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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). 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. 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.
机译：RNA测序（RNA-seq）技术已经鉴定出与复杂疾病相关的多个差异表达（DE）基因，但是，这些基因仅能解释差异的适度部分。全基因模型假设疾病可能由与疾病间接相关的基因驱动，并通过功能途径传播。在这里，我们专注于识别外部基因和功能途径之间的相互作用，是指基因-途径相互作用（GPI）。具体而言，依靠Garrote核机器（GKM）与方差成分检验和置换之间的关系，以获取分数统计的经验分布，我们提出了一种有效的分析程序，作为基于置换的二表型gEne-pAthway交互作用识别（PEA）。各种仿真表明，与传统的似然比测试（LRT）相比，PEA具有经过良好校准的I型错误率和更高的功率。此外，我们执行基因集富集算法和PEA，以从全癌数据（GES68086）中识别GPI。这些GPI和基因可能进一步说明了癌症的潜在病因，其中大多数已被鉴定，某些外部基因和重要途径与以前的研究一致。 PEA是从RNA序列数据中识别GPI的有效工具。可以进一步扩展它，以识别二进制表型的一个变量和一组其他组学数据的功能之间的相互作用。

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