Network-based functional enrichment.
Network-based functional enrichment.
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DOI：
10.1186/1471-2105-12-s13-s14
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发表时间：
2011
影响因子：
3
通讯作者：
Murali TM
中科院分区：
文献类型：
--
作者：
Poirel CL;Owens CC 3rd;Murali TM
人们已经发展了许多方法来推断和推理分子相互作用网络。这些方法通常会产生具有数百或数千个节点和高达一个数量级的多条边的网络。通常需要对此类网络中的生物信息进行汇总。一种非常常见的方法是使用基因功能丰富分析来完成这项任务。这种方法的一个主要缺点是它忽略了关于被分析网络中的边的信息，即它简单地将网络视为一组基因。在本文中，我们介绍了一种新的功能丰富方法，它显式地考虑了网络相互作用。我们的方法自然地推广了费舍尔的精确测试，这是一种基于基因集的技术。给定一个感兴趣的函数，我们计算由注解到该函数的基因所诱导的网络的子图。我们使用该子网络的连通组件的大小序列来估计其连通性。我们通过置换检验对连通性的统计意义进行了估计。我们给出了我们的方法的三个应用：i)确定在给定的网络中哪些功能被丰富，ii)给定一个网络和该网络中的一个有趣的基因子网络，确定哪些功能在该子网络中被丰富，以及iii)给定两个网络，确定当我们将第二个网络合并到第一个网络时，连通性改善的功能。通过这些应用，我们表明我们的方法是网络聚类算法的一种自然的替代方案。我们提出了一种新的功能丰富方法，该方法考虑了由特定功能注释的基因之间的成对关系。这三个应用程序中的每一个都发现了高度相关的功能。我们用我们的方法研究了来自三种不同生物的生物数据。我们的结果表明我们的方法具有广泛的适用性。我们的算法是用C++实现的，在GNU通用公共许可证下可以在我们的补充网站上免费获得。此外，我们所有的输入数据和结果都可以在http://bioinformatics.cs.vt.edu/~murali/supplements/2011-incob-nbe/.上找到
Many methods have been developed to infer and reason about molecular interaction networks. These approaches often yield networks with hundreds or thousands of nodes and up to an order of magnitude more edges. It is often desirable to summarize the biological information in such networks. A very common approach is to use gene function enrichment analysis for this task. A major drawback of this method is that it ignores information about the edges in the network being analyzed, i.e., it treats the network simply as a set of genes. In this paper, we introduce a novel method for functional enrichment that explicitly takes network interactions into account. Our approach naturally generalizes Fisher’s exact test, a gene set-based technique. Given a function of interest, we compute the subgraph of the network induced by genes annotated to this function. We use the sequence of sizes of the connected components of this sub-network to estimate its connectivity. We estimate the statistical significance of the connectivity empirically by a permutation test. We present three applications of our method: i) determine which functions are enriched in a given network, ii) given a network and an interesting sub-network of genes within that network, determine which functions are enriched in the sub-network, and iii) given two networks, determine the functions for which the connectivity improves when we merge the second network into the first. Through these applications, we show that our approach is a natural alternative to network clustering algorithms. We presented a novel approach to functional enrichment that takes into account the pairwise relationships among genes annotated by a particular function. Each of the three applications discovers highly relevant functions. We used our methods to study biological data from three different organisms. Our results demonstrate the wide applicability of our methods. Our algorithms are implemented in C++ and are freely available under the GNU General Public License at our supplementary website. Additionally, all our input data and results are available at http://bioinformatics.cs.vt.edu/~murali/supplements/2011-incob-nbe/.
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影响因子：
64.8
作者：
Soucek, Laura;Whitfield, Jonathan;Martins, Carla P.;Finch, Andrew J.;Murphy, Daniel J.;Sodir, Nicole M.;Karnezis, Anthony N.;Swigart, Lamorna Brown;Nasi, Sergio;Evan, Gerard I.
通讯作者：
Evan, Gerard I.
影响因子：
14.8
作者：
Margolin, Adam A.;Wang, Kai;Califano, Andrea
通讯作者：
Califano, Andrea
影响因子：
64.8
作者：
Collins, Sean R.;Miller, Kyle M.;Krogan, Nevan J.
通讯作者：
Krogan, Nevan J.
影响因子：
64.8
作者：
Rual, JF;Venkatesan, K;Vidal, M
通讯作者：
Vidal, M
影响因子：
9.9
作者：
通讯作者：
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