QSVA framework for RNA quality correction in differential expression analysis

Andrew E. Jaffe, Ran Tao, Alexis L. Norris, Marc Kealhofer, Abhinav Nellore, Joo Heon Shin, Dewey Kim, Yankai Jia, Thomas M. Hyde, Joel E. Kleinman, Richard E. Straub, Jeffrey T. Leek, Daniel R. Weinberger

Research output: Contribution to journalArticlepeer-review

59 Scopus citations


RNA sequencing (RNA-seq) is a powerful approach for measuring gene expression levels in cells and tissues, but it relies on highquality RNA. We demonstrate here that statistical adjustment using 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 molecular degradation experiments of human primary tissues, we introduce a method- quality surrogate variable analysis (qSVA)-as a framework for estimating and removing the confounding effect of RNA quality in differential expression analysis. We show that this approach results in greatly improved replication rates (>3×) across two large independent postmortem human brain studies of schizophrenia and also removes potential RNA quality biases in earlier published work that compared expression levels of different brain regions and other diagnostic groups. Our approach can therefore improve the interpretation of differential expression analysis of transcriptomic data from human tissue.

Original languageEnglish (US)
Pages (from-to)7130-7135
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number27
StatePublished - Jul 3 2017
Externally publishedYes


  • Differential expression analysis
  • RNA quality
  • RNA sequencing
  • Statistical modeling

ASJC Scopus subject areas

  • General


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