A comparison of background correction methods for two-colour microarrays
A comparison of background correction methods for two-colour microarrays
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DOI：
10.1093/bioinformatics/btm412
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发表时间：
2007-10-15
期刊：
影响因子：
5.8
通讯作者：
Smyth, Gordon K.
中科院分区：
文献类型：
--
作者：
Ritchie, Matthew E.;Silver, Jeremy;Smyth, Gordon K.
动机：微阵列数据必须进行背景校正，以消除阵列中非特异性结合或空间异质性的影响，但这种做法通常会导致其他问题，如负校正强度和低强度对数比的高变异性。不同的估计的背景，和各种基于模型的处理方法，在这项研究中进行比较，在寻找最佳的选择差异表达分析的小微阵列实验。结果如下：使用的数据，其中一些独立的基因表达的真相是已知的，八个不同的背景校正的替代品进行比较，在精度和偏差的基因表达的措施，并在他们的能力来检测差异表达的基因判断两个流行的算法，SAM和limma eBayes。介绍了一种新的基于卷积模型的背景处理方法（normexp）。基于模型的校正方法被证明是显着的上级减去当地的背景估计的通常做法。沿着沿着强度范围稳定对数比的方差的方法表现最好。normexp+offset方法的错误发现率最低，其次是morph和vsn。像vsn一样，normexp适用于大多数类型的双色微阵列数据。
Motivation: Microarray data must be background corrected to remove the effects of non-specific binding or spatial heterogeneity across the array, but this practice typically causes other problems such as negative corrected intensities and high variability of low intensity log-ratios. Different estimators of background, and various model-based processing methods, are compared in this study in search of the best option for differential expression analyses of small microarray experiments. Results: Using data where some independent truth in gene expression is known, eight different background correction alternatives are compared, in terms of precision and bias of the resulting gene expression measures, and in terms of their ability to detect differentially expressed genes as judged by two popular algorithms, SAM and limma eBayes. A new background processing method (normexp) is introduced which is based on a convolution model. The model-based correction methods are shown to be markedly superior to the usual practice of subtracting local background estimates. Methods which stabilize the variances of the log-ratios along the intensity range perform the best. The normexp+offset method is found to give the lowest false discovery rate overall, followed by morph and vsn. Like vsn, normexp is applicable to most types of two-colour microarray data.