A nonparametric significance test for sampled networks.
A nonparametric significance test for sampled networks.
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DOI：
10.1093/bioinformatics/btx419
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发表时间：
2018-01-01
期刊：
影响因子：
--
通讯作者：
Reed-Tsochas F
中科院分区：
文献类型：
--
作者：
Elliott A;Leicht E;Whitmore A;Reinert G;Reed-Tsochas F
我们的工作是出于对构建蛋白质-蛋白质相互作用网络的兴趣，该网络捕获了与帕金森病相关的关键特征。虽然有大量的子网络构建方法，但通常不清楚哪个子网络是进一步研究的最合适的起点。我们提供了一种方法来评估从种子列表（已知在感兴趣区域中重要的节点列表）构建的子网络是否与随机生成的子网络有显著差异。所提出的方法使用蒙特卡罗方法。由于不同的种子列表可以产生相同的子网络，我们通过构建最小种子列表作为显著性检验的起点来控制冗余。空模型基于随机种子列表，其长度与生成子网络的最小种子列表相同;在该随机种子列表中，节点具有与最小种子列表中的节点（近似）相同的度分布。我们使用这个空模型来选择子网络，这些子网络在一组适当的统计数据上显著偏离随机，并且可能为真实的世界蛋白质相互作用网络捕获有用的信息。本文中使用的软件可从https://sites.google.com/site/elliottande/下载。该软件是用Python编写的，使用NetworkX库。 补充数据可在Bioinformatics在线获得。
Our work is motivated by an interest in constructing a protein–protein interaction network that captures key features associated with Parkinson’s disease. While there is an abundance of subnetwork construction methods available, it is often far from obvious which subnetwork is the most suitable starting point for further investigation. We provide a method to assess whether a subnetwork constructed from a seed list (a list of nodes known to be important in the area of interest) differs significantly from a randomly generated subnetwork. The proposed method uses a Monte Carlo approach. As different seed lists can give rise to the same subnetwork, we control for redundancy by constructing a minimal seed list as the starting point for the significance test. The null model is based on random seed lists of the same length as a minimum seed list that generates the subnetwork; in this random seed list the nodes have (approximately) the same degree distribution as the nodes in the minimum seed list. We use this null model to select subnetworks which deviate significantly from random on an appropriate set of statistics and might capture useful information for a real world protein–protein interaction network. The software used in this paper are available for download at https://sites.google.com/site/elliottande/. The software is written in Python and uses the NetworkX library. Supplementary data are available at Bioinformatics online.
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影响因子：
3.7
作者：
Li BQ;Huang T;Liu L;Cai YD;Chou KC
通讯作者：
Chou KC
影响因子：
14.9
作者：
Chatr-Aryamontri A;Breitkreutz BJ;Heinicke S;Boucher L;Winter A;Stark C;Nixon J;Ramage L;Kolas N;O'Donnell L;Reguly T;Breitkreutz A;Sellam A;Chen D;Chang C;Rust J;Livstone M;Oughtred R;Dolinski K;Tyers M
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Tyers M
影响因子：
3.6
作者：
Bernard HR;Hallett T;Iovita A;Johnsen EC;Lyerla R;McCarty C;Mahy M;Salganik MJ;Saliuk T;Scutelniciuc O;Shelley GA;Sirinirund P;Weir S;Stroup DF
通讯作者：
Stroup DF
影响因子：
3
作者：
Berger SI;Posner JM;Ma'ayan A
通讯作者：
Ma'ayan A
DOI：
10.1073/pnas.0501179102
发表时间：
2005-03-22
影响因子：
11.1
作者：
Stumpf, MPH;Wiuf, C;May, RM
通讯作者：
May, RM