Keywords: Data integration, Graphical modeling, High-dimensional precision matrix estimation; Networks
Webpages:
https://CRAN.R-project.org/package=rags2ridges,
https://github.com/CFWP/rags2ridges Contact:
cf.peeters@vumc.nl A contemporary use for inverse covariance matrices (aka precision matrices) is found in the data-based reconstruction of networks through graphical modeling. Graphical models merge probability distributions of random vectors with graphs that express the conditional (in)dependencies between the constituent random variables. The
rags2ridges package enables L2-penalized (i.e., ridge) estimation of the precision matrix in settings where the number of variables is large relative to the sample size. Hence, it is a package where high-dimensional (HD) data meets networks.
The talk will give an overview of the
rags2ridges package. Specifically, it will show that the package is a one-stop-go as it provides functionality for the extraction, visualization, and analysis of networks from HD data. Moreover, it will show that the package provides a basis for the vertical (across data sets) and horizontal (across platforms) integration of HD data stemming from omics experiments. Last but not least, it will explain why many rap musicians are stating that one should ‘get
ridge, or die trying’.
References
https://arxiv.org/abs/1509.07982 https://arxiv.org/abs/1608.04123 http://dx.doi.org/10.1016/j.csda.2016.05.012
