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.
UCLA researchers have developed a method, which they 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. This 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. The researchers show that MSIQ provides a consistent estimator of isoform abundance, and they demonstrate the accuracy and effectiveness of MSIQ compared with alternative methods through simulation studies on D. melanogaster genes. They 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. The researchers also perform a comprehensive analysis of how the isoform quantification accuracy would be affected by RNA-seq sample heterogeneity and different experimental protocols.
Joint modeling of multiple RNA-seq samples
In this framework, Ed (d = 1, 2, …, D) is a binary hidden state variable indicating whether RNA-seq sample d is in the consistent group, while a, b and γ are hyper-parameters (priors) in Ed’s distribution. Depending on Ed, the isoform proportion vector τ(d) takes either the consistent group’s isoform proportion vector α or its own β(d). Given the isoform proportions, RNA-seq reads are generated in each sample, and our observed data are summarized as R(d)
Availability – The MSIQ method is implemented in the R package MSIQ, which is freely available at https://github.com/Vivianstats/MSIQ.