Chockalingam Sriram, Aluru Maneesha, Aluru Srinivas
Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai 40076, India.
School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Microarrays (Basel). 2016 Sep 19;5(3):23. doi: 10.3390/microarrays5030023.
Pre-processing of microarray data is a well-studied problem. Furthermore, all popular platforms come with their own recommended best practices for differential analysis of genes. However, for genome-scale network inference using microarray data collected from large public repositories, these methods filter out a considerable number of genes. This is primarily due to the effects of aggregating a diverse array of experiments with different technical and biological scenarios. Here we introduce a pre-processing pipeline suitable for inferring genome-scale gene networks from large microarray datasets. We show that partitioning of the available microarray datasets according to biological relevance into tissue- and process-specific categories significantly extends the limits of downstream network construction. We demonstrate the effectiveness of our pre-processing pipeline by inferring genome-scale networks for the model plant Arabidopsis thaliana using two different construction methods and a collection of 11,760 Affymetrix ATH1 microarray chips. Our pre-processing pipeline and the datasets used in this paper are made available at http://alurulab.cc.gatech.edu/microarray-pp.
微阵列数据的预处理是一个已得到充分研究的问题。此外，所有流行的平台都有其各自推荐的基因差异分析最佳实践方法。然而，对于使用从大型公共数据库收集的微阵列数据进行基因组规模的网络推断而言，这些方法会过滤掉相当数量的基因。这主要是由于将一系列具有不同技术和生物学背景的多样实验进行汇总所产生的影响。在此，我们介绍一种适用于从大型微阵列数据集中推断基因组规模基因网络的预处理流程。我们表明，根据生物学相关性将可用的微阵列数据集划分为组织特异性和过程特异性类别，可显著扩展下游网络构建的界限。我们通过使用两种不同的构建方法以及11760个Affymetrix ATH1微阵列芯片的集合，为模式植物拟南芥推断基因组规模网络，从而证明了我们预处理流程的有效性。我们的预处理流程以及本文中使用的数据集可在http://alurulab.cc.gatech.edu/microarray-pp获取。
