Normalization of Transcript Degradation Improves Accuracy in RNA-Seq Analysis

• 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): Ji-Ping Wang* and Bin Xiong and Yiben Yang
• Companies: Northwestern University and Northwestern University and Northwestern University
• Keywords: RNA-seq; normalization; nonnegative matrix factorization

Abstract

RNA-sequencing (RNA-seq) is a powerful high-throughput tool to profile transcriptional activities in cells. The observed read counts can be biased by various factors such that they do not accurately represent the true relative abundance of mRNA transcript abundance. Here we show that the gene-specific heterogeneity of transcript degradation pattern across samples presents a common and major source of unwanted variation, and it may substantially bias the results in gene expression analysis. Most existing normalization approaches focused on global adjustment of systematic bias are ineffective to correct for this bias. We propose a novel method based on matrix factorization over-approximation that allows quantification of RNA degradation of each gene within each sample. The estimated degradation index scores are used to build a pipeline named DeGNorm (stands for degradation normalization) to adjust read count for RNA degradation heterogeneity on a gene-by-gene basis while simultaneously controlling sequencing depth. The robust and effective performance of this method is demonstrated in an extensive set of real RNA-seq data and simulated data.