Quantifying Direct Dependencies in Biological Networks by Multiscale Association Analysis

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.