國立成功大學 統計與資料科學學系

Mediation analysis is performed to evaluate the effects of a hypothetical causal mechanism that
marks the progression from an exposure, through mediators, to an outcome. In the age of highthroughput technologies, it has become routine to assess numerous potential mechanisms at the genome or proteome scales. Alongside this, the necessity to address issues related to multiple testing has also arisen. In a sparse scenario where only a few genes or proteins are causally involved, conventional methods for assessing mediation effects lose statistical power because the composite null distribution behind this experiment can not be attained. The power loss hence decreases the true mechanisms identified after multiple testing corrections. To fairly delineate a uniform distribution under the composite null, Huang (2019, AoAS) proposed the composite test to provide adjusted p-values for single-mediator analyses. Our contribution is to extend the method to multi-mediator analyses, which are commonly encountered in genomic studies and also flexible to various biological interests. Using the generalized Berk-Jones statistics with the composite test, we proposed a multivariate approach that favors dense and diverse mediation effects, a decorrelation approach that favors sparse and consistent effects, and a hybrid approach that captures the edges of both approaches. Our analysis suite has been implemented as an R package MACtest. The utility is demonstrated by analyzing the lung adenocarcinoma datasets from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis
Consortium. We further investigate the genes and networks whose expression may be regulated by smoking-induced epigenetic aberrations.