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 accurate and 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 by 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 from human embryonic stem cells, brain tissues, and the HepG2 immortalized
cell line