Partial correlation (PC) or conditional mutual information (CMI) is widely used in detecting direct dependencies between the observed variables in biological networks by eliminating indirect correlations/associations, but it fails whenever there are some strong correlations in a network. In this paper, we theoretically develop a multiscale association analysis to overcome this flaw. We propose a new measure, partial association (PA), based on the multiscale conditional mutual information. We show that linear PA and nonlinear PA have clear advantages over PC and CMI from both theoretical and computational aspects. Both simulated models and real omics datasets demonstrate that PA is superior to PC and CMI in terms of accuracy, and is a powerful tool to identify the direct associations or reconstruct molecular networks based on the observed data. Survival and functional analyses of the hub genes in the gene networks reconstructed from TCGA data for different cancers also validated the effectiveness of our method.

中文翻译：

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通过多尺度关联分析量化生物网络中的直接依赖关系。

偏相关（PC）或条件互信息（CMI）被广泛用于通过消除间接相关/关联来检测生物网络中观察到的变量之间的直接依存关系，但是只要网络中存在一些强相关，它就会失败。在本文中，我们从理论上开发了一种多尺度关联分析来克服此缺陷。基于多尺度条件互信息，我们提出了一种新的度量，部分关联（PA）。我们从理论和计算两个方面证明，线性功率放大器和非线性功率放大器比PC和CMI具有明显的优势。模拟模型和实际的组学数据集都证明，PA在准确性方面优于PC和CMI，并且是基于观察到的数据识别直接关联或重建分子网络的强大工具。