Network science inspires novel tree shape statistics
Network science inspires novel tree shape statistics
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网络科学激发了新颖的树形统计
DOI：
10.1101/608646
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Chindelevitch L
中科院分区：
文献类型：
--
作者：
Chindelevitch L
系统发生树的形状可以用来获得进化的见解。树的形状指定了树的连通性，而它的分支长度反映了分支事件之间的时间或遗传距离;众所周知的树形状测量包括Colless和Sackin不平衡，它描述了树的不对称性。在其他情况下，网络科学已经成为描述网络结构特征的重要范式，并利用它们来理解复杂系统，从蛋白质相互作用到社会系统。因此，网络科学是许多描述树形状的新方法的潜在来源，因为树也是网络。在这里，我们从网络科学中定制工具，包括直径，平均路径长度，介数，接近度和特征向量中心性，以总结系统发育树的形状。因此，我们提出了树形的总结，是互补的不对称性和小配置的频率。这些新的统计量可以在线性时间内计算，并且可以很好地描述大树的形状。我们将这些统计数据与一些传统的树统计数据一起应用于三种非常不同的病毒（艾滋病毒，登革热和麻疹）的系统发育树，来自不同流行病学场景中的相同病毒（甲型流感和艾滋病毒）以及已知产生不同形状的树的模拟模型。使用互信息和监督学习算法，我们发现从网络科学改编的统计数据的表现与传统统计数据一样好或更好。我们描述了它们的分布，并证明了它们在树中极值的一些基本结果。我们的结论是，基于网络科学的树形摘要是树形特征工具包的一个很有前途的补充。我们所有的形状摘要，以及为两组树选择最有区别的形状的函数，都可以在http://github.com/Leonardini/treeCentrality上免费获得。
The shape of phylogenetic trees can be used to gain evolutionary insights. A tree’s shape specifies the connectivity of a tree, while its branch lengths reflect either the time or genetic distance between branching events; well-known measures of tree shape include the Colless and Sackin imbalance, which describe the asymmetry of a tree. In other contexts, network science has become an important paradigm for describing structural features of networks and using them to understand complex systems, ranging from protein interactions to social systems. Network science is thus a potential source of many novel ways to characterize tree shape, as trees are also networks. Here, we tailor tools from network science, including diameter, average path length, and betweenness, closeness, and eigenvector centrality, to summarize phylogenetic tree shapes. We thereby propose tree shape summaries that are complementary to both asymmetry and the frequencies of small configurations. These new statistics can be computed in linear time and scale well to describe the shapes of large trees. We apply these statistics, alongside some conventional tree statistics, to phylogenetic trees from three very different viruses (HIV, dengue fever and measles), from the same virus in different epidemiological scenarios (influenza A and HIV) and from simulation models known to produce trees with different shapes. Using mutual information and supervised learning algorithms, we find that the statistics adapted from network science perform as well as or better than conventional statistics. We describe their distributions and prove some basic results about their extreme values in a tree. We conclude that network science-based tree shape summaries are a promising addition to the toolkit of tree shape features. All our shape summaries, as well as functions to select the most discriminating ones for two sets of trees, are freely available as anRpackage at http://github.com/Leonardini/treeCentrality.
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DOI：
--
发表时间：
2009
期刊：
影响因子：
--
作者：
M. Stich;S. Manrubia
通讯作者：
S. Manrubia
影响因子：
4.3
作者：
Saulnier E;Gascuel O;Alizon S
通讯作者：
Alizon S
DOI：
--
发表时间：
2006
期刊：
IEEE/ACM Transactions on Computational Biology & Bioinformatics
影响因子：
--
作者：
Frederick Albert Matsen IV
通讯作者：
Frederick Albert Matsen IV
简短的交流：男男性行为的成年和青少年男性之间 HIV-1 传播的系统发育证据。
影响因子：
1.5
作者：
Wolf,Elizabeth;Herbeck,JoshuaT;VanRompaey,Stephen;Kitahata,Mari;Thomas,Katherine;Pepper,Gregory;Frenkel,Lisa
通讯作者：
Frenkel,Lisa
影响因子：
10.7
作者：
Poon AF
通讯作者：
Poon AF