SILGGM: Statistical Inference of Large-Scale Gaussian Graphical Model in Gene Networks

Provides a general framework to perform statistical inference of each gene pair and global inference of whole-scale gene pairs in gene networks using the well known Gaussian graphical model (GGM) in a time-efficient manner. We focus on the high-dimensional settings where p (the number of genes) is allowed to be far larger than n (the number of subjects). Four main approaches are supported in this package: (1) the bivariate nodewise scaled Lasso (Ren et al (2015) <doi:10.1214/14-AOS1286>) (2) the de-sparsified nodewise scaled Lasso (Jankova and van de Geer (2017) <doi:10.1007/s11749-016-0503-5>) (3) the de-sparsified graphical Lasso (Jankova and van de Geer (2015) <doi:10.1214/15-EJS1031>) (4) the GGM estimation with false discovery rate control (FDR) using scaled Lasso or Lasso (Liu (2013) <doi:10.1214/13-AOS1169>). Windows users should install 'Rtools' before the installation of this package.

Getting started

Package details

AuthorRong Zhang, Zhao Ren and Wei Chen
MaintainerRong Zhang <[email protected]>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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SILGGM documentation built on Nov. 17, 2017, 4:42 a.m.