Chung, LM, Ferguson, JP, Zheng, W, Qian, F, Bruno, V, Montgomery, RR and Zhao, HY (2013) Differential expression analysis for paired RNA-seq data. BMC Bioinformatics.

Abstract

Background: RNA-Seq technology measures the transcript abundance by generating sequence reads and counting their frequencies across different biological conditions. To identify differentially expressed genes between two conditions, it is important to consider the experimental design as well as the distributional property of the data. In many RNA-Seq studies, the expression data are obtained as multiple pairs, e. g., pre-vs. post-treatment samples from the same individual. We seek to incorporate paired structure into analysis. Results: We present a Bayesian hierarchical mixture model for RNA-Seq data to separately account for the variability within and between individuals from a paired data structure. The method assumes a Poisson distribution for the data mixed with a gamma distribution to account variability between pairs. The effect of differential expression is modeled by two-component mixture model. The performance of this approach is examined by simulated and real data. Conclusions: In this setting, our proposed model provides higher sensitivity than existing methods to detect differential expression. Application to real RNA-Seq data demonstrates the usefulness of this method for detecting expression alteration for genes with low average expression levels or shorter transcript length

Item Type:	Article
Additional Information:	pubid: 72 nvp_institute: NIBR contributor_address: [Chung, Lisa M.; Zhao, Hongyu] Yale Univ, Dept Biostat, Sch Publ Hlth, New Haven, CT 06520 USA. [Ferguson, John P.] George Washington Univ, Dept Stat, Washington, DC 20052 USA. [Zheng, Wei] Novartis Inst BioMed Res, Cambridge, MA USA. [Qian, Feng; Montgomery, Ruth R.] Yale Univ, Sch Med, Rheumatol Sect, New Haven, CT USA. [Bruno, Vincent] Univ Maryland, Sch Med, Dept Microbiol & Immunol, Baltimore, MD 21201 USA.
Date Deposited:	13 Oct 2015 13:13
Last Modified:	13 Oct 2015 13:13
URI:	https://oak.novartis.com/id/eprint/21915