MSIQ: Joint Modeling of Multiple RNA-seq Samples for Accurate Isoform Quantification

摘要

Next-generation RNA sequencing (RNA-seq) technology has been widely used toassess full-length RNA isoform abundance in a high-throughput manner. RNA-seqdata offer insight into gene expression levels and transcriptome structures,enabling us to better understand the regulation of gene expression andfundamental biological processes. Accurate isoform quantification from RNA-seqdata is challenging due to the information loss in sequencing experiments. Arecent accumulation of multiple RNA-seq data sets from the same tissue or celltype provides new opportunities to improve the accuracy of isoformquantification. However, existing statistical or computational methods formultiple RNA-seq samples either pool the samples into one sample or assignequal weights to the samples when estimating isoform abundance. These methodsignore the possible heterogeneity in the quality of different samples and couldresult in biased and unrobust estimates. In this article, we develop a method,which we call "joint modeling of multiple RNA-seq samples for accurate isoformquantification" (MSIQ), for more accurate and robust isoform quantification byintegrating multiple RNA-seq samples under a Bayesian framework. Our methodaims to (1) identify a consistent group of samples with homogeneous quality and(2) improve isoform quantification accuracy by jointly modeling multipleRNA-seq samples by allowing for higher weights on the consistent group. We showthat MSIQ provides a consistent estimator of isoform abundance, and wedemonstrate the accuracy and effectiveness of MSIQ compared with alternativemethods through simulation studies on D. melanogaster genes. We justify MSIQ'sadvantages over existing approaches via application studies on real RNA-seqdata from human embryonic stem cells, brain tissues, and the HepG2 immortalizedcell line.