Adjusting batch effects in microarray expression data using empirical Bayes methods
Adjusting batch effects in microarray expression data using empirical Bayes methods
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DOI：
10.1093/biostatistics/kxj037
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发表时间：
2007-01-01
期刊：
影响因子：
2.1
通讯作者：
Rabinovic, Ariel
中科院分区：
文献类型：
--
作者：
Johnson, W. Evan;Li, Cheng;Rabinovic, Ariel
非生物学实验变异或“批次效应”通常在多批次的微阵列实验中观察到，通常使得组合来自这些批次的数据的任务变得困难。联合收割机组合微阵列数据集的能力有利于研究人员增加统计能力，以从逻辑考虑限制样本大小的研究或需要阵列顺序杂交的研究中检测生物现象。一般来说，不调整批次效应就联合收割机组合数据集是不合适的。已经提出了从数据中过滤批量效应的方法，但这些方法通常很复杂，并且需要大批量（> 25）才能实现。由于大多数微阵列研究是使用小得多的样本量进行的，现有的方法是不够的。我们提出了参数和非参数经验贝叶斯框架调整数据的批量效应，是强大的离群值在小样本量和现有的方法进行大样本。我们使用两个示例数据集来说明我们的方法，并表明我们的方法是合理的，易于应用，在实践中是有用的。用于我们方法的软件可在http://biosun1.harvard.edu/complab/batch/免费获得。
Non-biological experimental variation or "batch effects" are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes (> 25) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at: http://biosun1.harvard.edu/complab/batch/.