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Proc Natl Acad Sci U S A. 2017 Jul 3;114(27):7130-7135. doi: 10.1073/pnas.1617384114. Epub 2017 Jun 20.

qSVA framework for RNA quality correction in differential expression analysis.

Jaffe AE1,2,3,4, Tao R5, Norris AL6,7, Kealhofer M5,8, Nellore A3,4,9, Shin JH5, Kim D5, Jia Y5, Hyde TM5,10,11, Kleinman JE5,11, Straub RE5, Leek JT3,4, Weinberger DR5,6,11,12.

Author information

1
Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205; andrew.jaffe@libd.org.
2
Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205.
3
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205.
4
Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21205.
5
Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205.
6
Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21205.
7
Department of Neurology, Kennedy Krieger Institute, Baltimore, MD 21205.
8
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205.
9
Department of Computer Science, Johns Hopkins University, Baltimore, MD 21205.
10
Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD 21205.
11
Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205.
12
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD 21205.

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 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.

KEYWORDS:

RNA quality; RNA sequencing; differential expression analysis; statistical modeling

PMID:
28634288
PMCID:
PMC5502589
DOI:
10.1073/pnas.1617384114
[Indexed for MEDLINE]
Free PMC Article

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