A framework for RNA quality correction in differential expression analysis
A framework for RNA quality correction in differential expression analysis
Abstract 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 postmortem human brain studies of schizophrenia. Finally, we explored public datasets to demonstrate potential RNA quality confounding when comparing expression levels of different brain regions and diagnostic groups beyond schizophrenia. Our approach can therefore improve the interpretation of differential expression analysis of transcriptomic data from the human brain.
Weinberger Daniel R.、Tao Ran、Leek Jeffrey T.、Jaffe Andrew E.、Norris Alexis L.、Kealhofer Marc、Kleinman Joel E.、Hyde Thomas M.、Nellore Abhinav、Jia Yankai、Straub Richard E.
Lieber Institute for Brain Development, Johns Hopkins Medical Campus||Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine||Department of Neuroscience, Johns Hopkins School of Medicine||McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of MedicineLieber Institute for Brain Development, Johns Hopkins Medical CampusDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health||Center for Computational Biology, Johns Hopkins UniversityLieber Institute for Brain Development, Johns Hopkins Medical Campus||Department of Mental Health, Johns Hopkins Bloomberg School of Public Health||Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health||Center for Computational Biology, Johns Hopkins UniversityDepartment of Neuroscience, Johns Hopkins School of Medicine||Department of Neurology, Kennedy Krieger InstituteLieber Institute for Brain Development, Johns Hopkins Medical Campus||Department of Epidemiology, Johns Hopkins Bloomberg School of Public HealthLieber Institute for Brain Development, Johns Hopkins Medical Campus||Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of MedicineLieber Institute for Brain Development, Johns Hopkins Medical Campus||Department of Neurology, Johns Hopkins School of Medicine||Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of MedicineDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health||Center for Computational Biology, Johns Hopkins University||Department of Computer Science, Johns Hopkins UniversityLieber Institute for Brain Development, Johns Hopkins Medical CampusLieber Institute for Brain Development, Johns Hopkins Medical Campus
基础医学生物科学研究方法、生物科学研究技术神经病学、精神病学
Weinberger Daniel R.,Tao Ran,Leek Jeffrey T.,Jaffe Andrew E.,Norris Alexis L.,Kealhofer Marc,Kleinman Joel E.,Hyde Thomas M.,Nellore Abhinav,Jia Yankai,Straub Richard E..A framework for RNA quality correction in differential expression analysis[EB/OL].(2025-03-28)[2025-05-23].https://www.biorxiv.org/content/10.1101/074245.点此复制
评论