A framework for RNA quality correction in differential expression analysis

RNA sequencing (RNA-seq) is a powerful approach for measuring gene expression levels in cells and tissues, but it relies on high-quality RNA. We demonstrate here that statistical adjustment employing existing quality measures largely fails to remove the effects of RNA degradation when RNA quality associates with the outcome of interest. Using RNA-seq data from a molecular degradation experiment of human brain tissue, we introduce the quality surrogate variable (qSVA) analysis framework for estimating and removing the confounding effect of RNA quality in differential expression analysis. We show this approach results in greatly improved replication rates (>3x) across two large independent pos