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March 2018 MSIQ: Joint modeling of multiple RNA-seq samples for accurate isoform quantification
Wei Vivian Li, Anqi Zhao, Shihua Zhang, Jingyi Jessica Li
Ann. Appl. Stat. 12(1): 510-539 (March 2018). DOI: 10.1214/17-AOAS1100


Next-generation RNA sequencing (RNA-seq) technology has been widely used to assess full-length RNA isoform abundance in a high-throughput manner. RNA-seq data offer insight into gene expression levels and transcriptome structures, enabling us to better understand the regulation of gene expression and fundamental biological processes. Accurate isoform quantification from RNA-seq data is challenging due to the information loss in sequencing experiments. A recent accumulation of multiple RNA-seq data sets from the same tissue or cell type provides new opportunities to improve the accuracy of isoform quantification. However, existing statistical or computational methods for multiple RNA-seq samples either pool the samples into one sample or assign equal weights to the samples when estimating isoform abundance. These methods ignore the possible heterogeneity in the quality of different samples and could result in biased and unrobust estimates. In this article, we develop a method, which we call “joint modeling of multiple RNA-seq samples for accurate isoform quantification” (MSIQ), for more robust isoform quantification by integrating multiple RNA-seq samples under a Bayesian framework. Our method aims to (1) identify a consistent group of samples with homogeneous quality and (2) improve isoform quantification accuracy by jointly modeling multiple RNA-seq samples and allowing for higher weights on the consistent group. We show that MSIQ provides a consistent estimator of isoform abundance, and we demonstrate the accuracy and effectiveness of MSIQ compared with alternative methods through simulation studies on D. melanogaster genes. We justify MSIQ’s advantages over existing approaches via application studies on real RNA-seq data of human embryonic stem cells, brain tissues, and the HepG2 immortalized cell line. We also perform a comprehensive analysis of how the isoform quantification accuracy would be affected by RNA-seq sample heterogeneity and different experimental protocols.


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Wei Vivian Li. Anqi Zhao. Shihua Zhang. Jingyi Jessica Li. "MSIQ: Joint modeling of multiple RNA-seq samples for accurate isoform quantification." Ann. Appl. Stat. 12 (1) 510 - 539, March 2018.


Received: 1 November 2016; Revised: 1 September 2017; Published: March 2018
First available in Project Euclid: 9 March 2018

zbMATH: 06894716
MathSciNet: MR3773403
Digital Object Identifier: 10.1214/17-AOAS1100

Keywords: Bayesian hierarchical models , data heterogeneity , Gibbs sampling , Isoform abundance estimation , joint inference from multiple samples , RNA sequencing

Rights: Copyright © 2018 Institute of Mathematical Statistics


Vol.12 • No. 1 • March 2018
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