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Title: A Bayesian approach to identify genes with multiple expression patterns for paired RNA-seq data Authors:  Jing Qiu - University of Delaware (United States) [presenting]
Zhuoqiong He - University of Missouri (United States)
Yuanyuan Bian - University of Missouri (United States)
Abstract: It is often of interest to identify genes with specific expression patterns over several conditions such as time points, genotypes, etc. The common practice is to perform differential expression analysis separately for each condition and then combine the results to obtain a list of genes with desired expression pattern or profiles. Such practice can inflate the type I error for identifying genes with different expression patterns under multiple conditions, especially when the desired expression pattern involves equally expression under certain conditions. We propose a Bayesian approach to identify genes with multiple expression patterns under two conditions with FDR controlled for all desired expression patterns simultaneously. The inverse moment non-local prior is used for modeling expression patterns with equal expression under one condition. Our simulation studies show that it is a much more challenging job to identify genes that are equally expressed in one condition but differentially expressed in the other condition than identify genes that are differentially expressed in both conditions. The common practice in literature can have highly inflated type I error for identifying the former type of genes. Our method has FDR controlled close to the nominal level with better power than the popular methods in the literature.