Discovering study-specific gene regulatory networks.
Discovering study-specific gene regulatory networks.
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DOI：
10.1371/journal.pone.0106524
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Tucker A
中科院分区：
文献类型：
--
作者：
Bo V;Curtis T;Lysenko A;Saqi M;Swift S;Tucker A
微阵列通常用于生物学，因为它们能够在不同条件下同时测量数千个基因。由于其结构，通常包含大量的变量，但样本少得多，可扩展的网络分析技术经常被采用。特别是，最近已经使用了共识方法，该方法结合了联合收割机多个微阵列研究，以找到更鲁棒的网络。然而，本文的目的是联合收割机多个微阵列的研究，自动识别子网络是独特的特定的实验条件，而不是共同的。为了更好地理解关键的调控机制以及它们在不同条件下如何变化，我们从使用glasso构建的多个独立网络中获得了独特的网络，这超出了标准的相关性。这涉及计算聚类预测精度，以检测特定条件下最具预测性的基因。我们区分使用交叉验证计算的准确性在一个选定的研究集群（帧内预测准确性）和那些属于不同的研究集群（帧间预测准确性）的一组独立的研究计算。最后，我们将我们的方法的结果与相关的最先进的技术进行比较。我们探讨了建议的管道如何在合成数据和真实的数据（小麦和镰刀菌）上执行。我们的研究结果表明，子网络可以可靠地识别特定的研究子集，这些网络反映了关键的机制，这些机制是基本的实验条件，在每个子集。
Microarrays are commonly used in biology because of their ability to simultaneously measure thousands of genes under different conditions. Due to their structure, typically containing a high amount of variables but far fewer samples, scalable network analysis techniques are often employed. In particular, consensus approaches have been recently used that combine multiple microarray studies in order to find networks that are more robust. The purpose of this paper, however, is to combine multiple microarray studies to automatically identify subnetworks that are distinctive to specific experimental conditions rather than common to them all. To better understand key regulatory mechanisms and how they change under different conditions, we derive unique networks from multiple independent networks built using glasso which goes beyond standard correlations. This involves calculating cluster prediction accuracies to detect the most predictive genes for a specific set of conditions. We differentiate between accuracies calculated using cross-validation within a selected cluster of studies (the intra prediction accuracy) and those calculated on a set of independent studies belonging to different study clusters (inter prediction accuracy). Finally, we compare our method's results to related state-of-the art techniques. We explore how the proposed pipeline performs on both synthetic data and real data (wheat and Fusarium). Our results show that subnetworks can be identified reliably that are specific to subsets of studies and that these networks reflect key mechanisms that are fundamental to the experimental conditions in each of those subsets.
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