Castelo Robert, Roverato Alberto
Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain.
J Comput Biol. 2009 Feb;16(2):213-27. doi: 10.1089/cmb.2008.08TT.
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.xsedu/qpgraph).
应用于高通量实验数据的逆向工程生物信息学程序，已成为生成有关分子调控机制新假设的重要手段。基因表达微阵列数据尤其如此，为了辅助构建转录调控网络模型，已开发了大量统计和计算方法。每个不同程序面临的一个主要挑战是，用于估计网络模型的可用样本数量n，远小于构成所研究系统的基因数量p。这损害了许多方法所依赖的统计假设，常常导致性能数据不稳定。在这项工作中，我们应用了一种最近开发的基于所谓q阶有限偏相关图（qp图）的新方法，该方法专门针对从p >> n的微阵列表达数据中发现分子网络而设计。利用大肠杆菌的实验和功能注释数据，我们在此展示了，当基因与实验的比例超过一个数量级时，qp图如何比其他现有方法产生更稳定的性能数据。更重要的是，我们还表明，qp图方法在这种基因与样本比例下的更好性能，对逆向工程转录调控模块的功能连贯性具有决定性影响，并且在这种具有挑战性的情况下至关重要，以便能够发现一个具有合理可信度的网络，该网络包含大量与所测试条件相关的基因。一个名为qpgraph的R包实现了此方法，它是Bioconductor项目的一部分，可从(www.bioconductor.org)下载。一个用于最耗费计算资源的计算的并行独立版本可从(http://functionalgenomics.upf.xsedu/qpgraph)获得。
