Reverse Engineering Molecular Regulatory Networks from Microarray Data with qp-Graphs
Reverse Engineering Molecular Regulatory Networks from Microarray Data with qp-Graphs
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DOI：
10.1089/cmb.2008.08tt
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发表时间：
2009-02-01
影响因子：
1.7
通讯作者：
Roverato, Alberto
中科院分区：
文献类型：
--
作者：
Castelo, Robert;Roverato, Alberto
应用于高通量实验数据的反向工程生物信息学程序已成为产生关于分子调控机制的新假说的重要工具。尤其是基因表达微阵列数据，已经开发了大量的统计和计算方法，以帮助建立转录调控的网络模型。每个不同程序面临的一个主要挑战是，可用于估计网络模型的样本n的数量远远少于形成所研究的系统的基因p的数量。这损害了方法的统计数据所依赖的许多假设，经常导致性能数据不稳定。在这项工作中，我们应用了最近开发的一种新的方法，基于所谓的q顺序限制偏相关图，qp图，它是专门为从p>；>；n微阵列表达数据中发现分子网络而量身定做的。使用来自大肠杆菌的实验和功能注释数据，这里我们展示了当基因与实验的比率超过一个数量级时，qp图如何比其他最先进的方法产生更稳定的性能数据。更重要的是，我们还表明，在这样的基因与样本比率上，qp图方法的更好性能对反向工程转录调控模块的功能一致性具有决定性的影响，并在这样一个具有挑战性的情况下变得至关重要，以便能够发现一个合理的置信度网络，其中包括与所述条件相关的大量基因。实现这种方法的名为qpgraph的R包是BioConductor项目的一部分，可以从www.Bioconductor.org下载。Http://functionalgenomics.upf.edu/qpgraph.提供了用于计算成本最高的计算的并行独立版本
Reverse engineering bioinformatic procedures applied to high-throughput experimental data have become instrumental in generating new hypotheses about molecular regulatory mechanisms. This has been particularly the case for gene expression microarray data, where a large number of statistical and computational methodologies have been developed in order to assist in building network models of transcriptional regulation. A major challenge faced by every different procedure is that the number of available samples n for estimating the network model is much smaller than the number of genes p forming the system under study. This compromises many of the assumptions on which the statistics of the methods rely, often leading to unstable performance figures. In this work, we apply a recently developed novel methodology based in the so-called q-order limited partial correlation graphs, qp-graphs, which is specifically tailored towards molecular network discovery from microarray expression data with p >> n. Using experimental and functional annotation data from Escherichia coli, here we show how qp-graphs yield more stable performance figures than other state-of-the-art methods when the ratio of genes to experiments exceeds one order of magnitude. More importantly, we also show that the better performance of the qp-graph method on such a gene-to-sample ratio has a decisive impact on the functional coherence of the reverse-engineered transcriptional regulatory modules and becomes crucial in such a challenging situation in order to enable the discovery of a network of reasonable confidence that includes a substantial number of genes relevant to the essayed conditions. An R package, called qpgraph implementing this method is part of the Bioconductor project and can be downloaded from www.bioconductor.org. A parallel standalone version for the most computationally expensive calculations is available from http://functionalgenomics.upf.edu/qpgraph.