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A hierarchical method for finding interactions: Jointly using linear correlation and rank correlation analysis

机译:查找交互的分层方法:联合使用线性相关和秩相关分析

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摘要

In the earlier studies, I pointed out that a network changed in a local domain can be approximated as a linear network, i.e., all between-node (or -taxon, -component, etc) changes in the local domain are treated as linear ones and Pearson linear correlation measure can be used. For a little wider domain, the quasi-linear measure, Spearman rank correlation can be used also. In present study, I jointly use Pearson linear correlation measure and Spearman rank correlation measure and their partial correlations to find interactions. First, I define some hierarchical principles for finding interactions. Reliability levels are then defined using set operations. The full algorithm and Matlab codes for finding interactions are given.
机译:在较早的研究中,我指出本地网络中变化的网络可以近似为线性网络,即本地网络中所有节点间(或-taxon,-component等)变化都被视为线性网络。可以使用Pearson线性相关度量。对于较宽的域,也可以使用准线性度量,Spearman秩相关。在本研究中,我联合使用Pearson线性相关度量和Spearman秩相关度量以及它们的偏相关来查找相互作用。首先,我定义了一些用于查找交互的层次结构原则。然后使用设置操作定义可靠性级别。给出了用于查找交互的完整算法和Matlab代码。

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