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 5 articles )
Showing results 1 to 5 of 5.
- Tyler Grimes, Somnath Datta: SeqNet: An R Package for Generating Gene-Gene Networks and Simulating RNA-Seq Data (2021) not zbMATH
- Bodwin, Kelly; Zhang, Kai; Nobel, Andrew: A testing based approach to the discovery of differentially correlated variable sets (2018)
- Jalal K. Siddiqui, Elizabeth Baskin, Mingrui Liu, Carmen Z. Cantemir-Stone, Bofei Zhang, Russell Bonneville, Joseph P. McElroy, Kevin R. Coombes, Ewy A. Mathé: IntLIM: Integration using Linear Models of metabolomics and gene expression data (2018) arXiv
- 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)