DiffCorr: Analyzing and Visualizing Differential Correlation Networks in Biological Data. A method for identifying pattern changes between 2 experimental conditions in correlation networks (e.g., gene co-expression networks), which builds on a commonly used association measure, such as Pearson’s correlation coefficient. This package includes functions to calculate correlation matrices for high-dimensional dataset and to test differential correlation, which means the changes in the correlation relationship among variables (e.g., genes and metabolites) between 2 experimental conditions.
Keywords for this software
References in zbMATH (referenced in 2 articles )
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- Dehmer, Matthias (ed.); Shi, Yongtang (ed.); Emmert-Streib, Frank (ed.): Computational network analysis with R. Applications in biology, medicine and chemistry (2017)
- Cai, T.Tony; Zhang, Anru: Inference for high-dimensional differential correlation matrices (2016)