Using control genes to correct for unwanted variation in microarray data
Using control genes to correct for unwanted variation in microarray data
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DOI：
10.1093/biostatistics/kxr034
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发表时间：
2012-07-01
期刊：
影响因子：
2.1
通讯作者：
Speed, Terence P.
中科院分区：
文献类型：
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作者：
Gagnon-Bartsch, Johann A.;Speed, Terence P.
微阵列表达研究遭受批量效应和其他不想要的变化的问题。已经提出了许多方法来调整微阵列数据，以减轻不必要的变化的问题。这些方法中有几种依赖于因子分析来从数据中推断出不必要的变化。这种方法的一个核心问题是很难从研究人员感兴趣的生物变异中辨别出不需要的变异。我们提出了一种新的方法，用于差异表达研究，试图克服这个问题，限制因素分析阴性对照基因。阴性对照基因是先验已知相对于感兴趣的生物因子不差异表达的基因。因此，这些基因表达水平的变异可以被认为是不需要的变异。我们将此方法命名为“删除不需要的变化，2步”（RUV-2）。我们讨论了各种技术评估的调整方法的性能和比较RUV-2的性能与其他常用的调整方法，如战斗和替代变量分析（SVA）。我们提出了几个例子的研究，每一个关于基因的差异表达与性别在大脑中，并发现RUV-2执行以及或优于其他方法。最后，我们讨论了调整RUV-2用于不关心差异表达的研究的可能性，并得出结论，可能有希望，但仍然存在重大挑战。
Microarray expression studies suffer from the problem of batch effects and other unwanted variation. Many methods have been proposed to adjust microarray data to mitigate the problems of unwanted variation. Several of these methods rely on factor analysis to infer the unwanted variation from the data. A central problem with this approach is the difficulty in discerning the unwanted variation from the biological variation that is of interest to the researcher. We present a new method, intended for use in differential expression studies, that attempts to overcome this problem by restricting the factor analysis to negative control genes. Negative control genes are genes known a priori not to be differentially expressed with respect to the biological factor of interest. Variation in the expression levels of these genes can therefore be assumed to be unwanted variation. We name this method "Remove Unwanted Variation, 2-step" (RUV-2). We discuss various techniques for assessing the performance of an adjustment method and compare the performance of RUV-2 with that of other commonly used adjustment methods such as Combat and Surrogate Variable Analysis (SVA). We present several example studies, each concerning genes differentially expressed with respect to gender in the brain and find that RUV-2 performs as well or better than other methods. Finally, we discuss the possibility of adapting RUV-2 for use in studies not concerned with differential expression and conclude that there may be promise but substantial challenges remain.