Abstract
BackgroundDifferential correlation networks are increasingly used to delineate changes in interactions among biomolecules. They characterize differences between omics networks under two different conditions, and can be used to delineate mechanisms of disease initiation and progression.ResultsWe present a new R package, CorDiffViz, that facilitates the estimation and visualization of differential correlation networks using multiple correlation measures and inference methods. The software is implemented in R, HTML and Javascript, and is available at https://github.com/sqyu/CorDiffViz. Visualization has been tested for the Chrome and Firefox web browsers. A demo is available at https://diffcornet.github.io/CorDiffViz/demo.html.ConclusionsOur software offers considerable flexibility by allowing the user to interact with the visualization and choose from different estimation methods and visualizations. It also allows the user to easily toggle between correlation networks for samples under one condition and differential correlations between samples under two conditions. Moreover, the software facilitates integrative analysis of cross-correlation networks between two omics data sets.
Highlights
Differential correlation networks are increasingly used to delineate changes in interactions among biomolecules
The user may run the function multiple times on multiple datasets by assigning a different name to each run; each run can be visualized by selecting it from a dropdown menu in viz.html, which is automatically generated by the package
The second application demonstrates how CorDiffViz can be used for differential cross-correlation analysis among two omics data sets, a setting of increasing interest for which public estimation and visualization software tools are lacking
Summary
Differential correlation networks are increasingly used to delineate changes in interactions among biomolecules They characterize differences between omics networks under two different conditions, and can be used to delineate mechanisms of disease initiation and progression. Differential correlation networks capture differences between omics correlations in two populations/conditions, e.g., cases and controls [1, 2] They can be used to gain insight into aberrations in biological processes and mechanisms of disease initiation and progression [3]. They have been instrumental in gaining insights into biological responses to environmental factors [4, 5] or functional consequences of mutations [6, 7] This has led to the development of multiple methods for differential correlation analysis in recent years [8,9,10,11,12,13,14]; see [2, 15] for more comprehensive review. Existing software either only focus on a single omics data type (commonly, mRNA expressions) and do not facilitate integrative
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