SILGGM: Statistical Inference of Large-Scale Gaussian Graphical Model in Gene Networks
Version 1.0.0

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) ) (2) the de-sparsified nodewise scaled Lasso (Jankova and van de Geer (2017) ) (3) the de-sparsified graphical Lasso (Jankova and van de Geer (2015) ) (4) the GGM estimation with false discovery rate control (FDR) using scaled Lasso or Lasso (Liu (2013) ). Windows users should install 'Rtools' before the installation of this package.

Getting started

Package details

AuthorRong Zhang, Zhao Ren and Wei Chen
Date of publication2017-10-16 11:49:17 UTC
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.