qSVA 框架用于差异表达分析中的 RNA 质量校正。
qSVA framework for RNA quality correction in differential expression analysis.
机构信息
Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205;
Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205.
出版信息
Proc Natl Acad Sci U S A. 2017 Jul 3;114(27):7130-7135. doi: 10.1073/pnas.1617384114. Epub 2017 Jun 20.
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.
RNA 测序（RNA-seq）是一种测量细胞和组织中基因表达水平的强大方法，但它依赖于高质量的 RNA。我们在这里证明，当 RNA 质量与感兴趣的结果相关时，使用现有质量指标进行统计调整在很大程度上无法消除 RNA 降解的影响。使用来自人类原代组织分子降解实验的 RNA-seq 数据，我们引入了一种方法——质量替代变量分析（qSVA）——作为一种估计和去除差异表达分析中 RNA 质量混杂效应的框架。我们表明，这种方法在两项关于精神分裂症的大型独立的人类大脑死后研究中大大提高了复制率（>3×），并且还消除了之前在比较不同大脑区域和其他诊断组的表达水平的工作中潜在的 RNA 质量偏差。因此，我们的方法可以改善对人类组织转录组数据差异表达分析的解释。
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