A Joint Bayesian Model for Integrating Microarray and RNA Sequencing Transcriptomic Data.

ABSTRACT: As the sequencing cost continued to drop in the past decade, RNA sequencing (RNA-seq) has replaced microarray to become the standard high-throughput experimental tool to analyze transcriptomic profile. As more and more datasets are generated and accumulated in the public domain, meta-analysis to combine multiple transcriptomic studies to increase statistical power has received increasing popularity. In this article, we propose a Bayesian hierarchical model to jointly integrate microarray and RNA-seq studies. Since systematic fold change differences across RNA-seq and microarray for detecting differentially expressed genes have been previously reported, we replicated this finding in several real datasets and showed that incorporation of a normalization procedure to account for the bias impr

SUBMITTER: Ma T 

PROVIDER: S-EPMC5510692 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature