CorDiffViz: an R package for visualizing multi-omics differential correlation networks

• Main Authors: Drton, M. (Author), Promislow, D.E.L (Author), Shojaie, A. (Author), Yu, S. (Author)
• Format: Article
• Language: English
• Published: BioMed Central Ltd 2021
• Subjects: Condition; Correlation measures; Correlation network; Correlation networks; Data integration; Data visualization; Differential correlation; Differential correlations; Disease initiations; Disease progression; HTML; HTTP; Inference methods; Multiple correlation; 'omics'; Omics; software; Software; Undirected graph; Undirected graphs; Visualization; web browser; Web Browser; Web browsers
• Online Access: View Fulltext in Publisher

 Background: 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. Results: We 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. Conclusions: Our 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. © 2021, The Author(s).