Joint Modeling of Multiple RNA-Seq Samples for Accurate Isoform Quantification

• Activity Number: 490 - Advances in Methods for the Accurate Measurement of High-Throughput Sequencing Data
• Type: Contributed
• Date/Time: Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Wei Li* and Jingyi Li and Anqi Zhao and Shihua Zhang
• Companies: University of California, Los Angeles and University of California, Los Angeles and Harvard University and Chinese Academy of Sciences
• Keywords: RNA-seq; isoform quantification; Bayesian method; hierarchical model

Abstract

Next-generation RNA sequencing (RNA-seq) data offer insight into gene expression and transcriptome structures, enabling us to better understand the regulation of fundamental biological processes. A recent accumulation of multiple RNA-seq datasets from the same tissue or cell type provides new opportunities to improve the accuracy of isoform quantification. However, existing statistical 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 estimates. We develop MSIQ for more robust isoform quantification by integrating multiple samples under a Bayesian framework. Our method aims to (1) identify a consistent group of samples with homogeneous quality and (2) improve isoform quantification by jointly modeling multiple 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 of MSIQ compared with alternative methods via applications on both simulated and real data.