A simple method to integrate different versions of Affymetrix microarrays using duplicate samples

The size and scope of microarray experiments continue to increase. However, datasets generated on different platforms or at different centres contain biases. Improved techniques are needed to remove platform- and batch-specific biases. One experimental control is the replicate hybridization of a subset of samples at each site or on each platform to learn the relationship between the two platforms. To date, no algorithm exists to specifically use this type of control. LTR is a linear-modelling-based algorithm that learns the relationship between different microarray batches from replicate hybridizations. LTR was tested on a new benchmark dataset of 20 samples hybridized to different Affymetrix microarray platforms. Before LTR, the two platforms were significantly different; application of LTR removed this bias. LTR was tested with six separate data pre-processing algorithms, and its effectiveness was independent of the pre-processing algorithm. Sample-size experiments indicate that just three replicate hybridizations can significantly reduce bias. An R library implementing LTR is available.微阵列实验的规模与覆盖范围持续扩大。然而，不同平台或不同实验中心生成的数据集往往存在偏差，亟需开发更优技术以消除平台特异性与批次特异性偏差。一种常用的实验对照方案为：在每个实验站点或每个平台上对部分样本进行重复杂交，以推导不同平台间的关联关系。迄今为止，尚无专门针对此类对照设计的算法。LTR是一种基于线性建模的算法，能够从重复杂交实验数据中学习不同微阵列批次间的关联模式。研究人员使用一组包含20个样本的全新基准数据集对LTR进行测试，这些样本分别在不同的Affymetrix微阵列平台上完成了杂交。在未使用LTR前，两个平台的检测结果存在显著差异；应用LTR后，该偏差被成功消除。研究人员同时使用6种独立的数据预处理算法对LTR进行验证，结果显示LTR的校正效果不受预处理算法的影响。样本量对照实验表明，仅需3次重复杂交即可显著降低偏差。目前已公开一款实现了LTR算法的R语言库。