Systematic noise degrades gene co-expression signals but can be corrected.
Systematic noise degrades gene co-expression signals but can be corrected.
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DOI：
10.1186/s12859-015-0745-3
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发表时间：
2015-09-24
影响因子：
3
通讯作者：
Bahlo M
中科院分区：
文献类型：
--
作者：
Freytag S;Gagnon-Bartsch J;Speed TP;Bahlo M
在过去的十年中，基因共表达的鉴定已经成为高维芯片数据分析的常规部分。基因共表达主要通过Pearson相关系数检测，在发现分子通路和网络方面发挥了重要作用。不幸的是，高维微阵列数据集中系统噪声的存在破坏了基因共表达的估计。因此，从微阵列数据中去除系统噪声至关重要。存在许多微阵列数据的清洗方法，但是这些方法旨在改善差异表达分析，并且它们的性能已主要用于此应用测试。据我们所知，这些方法的性能从未在基因共表达估计的背景下进行系统的比较。通过模拟，我们证明了标准的清理程序，如背景校正和分位数归一化，不能充分去除影响基因共表达的系统噪声，有时还会进一步降低真正的基因共表达。相反，我们展示了去除不必要变异（RUV）的全局版本，一种数据驱动的方法，消除了系统噪声，但也允许估计真正的潜在基因-基因相关性。我们比较了所有噪声去除方法在应用于人类大脑中基因表达的五个大型公开数据集时的性能。RUV检索已知相互作用的基因集的最高基因共表达值，但也在所有五个数据集之间提供最大的一致性。应用该方法对癫痫性脑病候选基因进行排序。我们的工作引起了人们对许多已发表的基因共表达分析质量的严重关注。当目标是基因共表达分析时，RUV提供了一种有效而灵活的方法来去除高维微阵列数据集中的系统噪声。RUV方法适用于基因-基因相关性估计，可作为BioconductoR-package: RUVcorr。本文的在线版本（doi:10.1186/s12859-015-0745-3）包含补充材料，授权用户可以使用。
In the past decade, the identification of gene co-expression has become a routine part of the analysis of high-dimensional microarray data. Gene co-expression, which is mostly detected via the Pearson correlation coefficient, has played an important role in the discovery of molecular pathways and networks. Unfortunately, the presence of systematic noise in high-dimensional microarray datasets corrupts estimates of gene co-expression. Removing systematic noise from microarray data is therefore crucial. Many cleaning approaches for microarray data exist, however these methods are aimed towards improving differential expression analysis and their performances have been primarily tested for this application. To our knowledge, the performances of these approaches have never been systematically compared in the context of gene co-expression estimation. Using simulations we demonstrate that standard cleaning procedures, such as background correction and quantile normalization, fail to adequately remove systematic noise that affects gene co-expression and at times further degrade true gene co-expression. Instead we show that a global version of removal of unwanted variation (RUV), a data-driven approach, removes systematic noise but also allows the estimation of the true underlying gene-gene correlations. We compare the performance of all noise removal methods when applied to five large published datasets on gene expression in the human brain. RUV retrieves the highest gene co-expression values for sets of genes known to interact, but also provides the greatest consistency across all five datasets. We apply the method to prioritize epileptic encephalopathy candidate genes. Our work raises serious concerns about the quality of many published gene co-expression analyses. RUV provides an efficient and flexible way to remove systematic noise from high-dimensional microarray datasets when the objective is gene co-expression analysis. The RUV method as applicable in the context of gene-gene correlation estimation is available as a BioconductoR-package: RUVcorr. The online version of this article (doi:10.1186/s12859-015-0745-3) contains supplementary material, which is available to authorized users.