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

作者：Ma Tianzhou; Liang Faming; Oesterreich Steffi; Tseng George C

来源：Journal of Computational Biology, 2017, 24(7): 647-662.

DOI：10.1089/cmb.2017.0056

• Bayesian hierarchical model
• differential expression (DE)
• meta-analysis
• microarray
• normalization
• RNA sequencing (RNA-seq)

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 improves the detection accuracy and power. We compared our method with the popular two-stage Fisher's method using simulations and two real applications in a histological subtype (invasive lobular carcinoma) of breast cancer comparing PR+ versus PR- and early-stage versus late-stage patients. The result showed improved detection power and more significant and interpretable pathways enriched in the detected biomarkers from the proposed Bayesian model.

• 出版日期2017-7