SILGGM: An extensive R package for efficient statistical inference in large-scale gene networks.

ABSTRACT: Gene co-expression network analysis is extremely useful in interpreting a complex biological process. The recent droplet-based single-cell technology is able to generate much larger gene expression data routinely with thousands of samples and tens of thousands of genes. To analyze such a large-scale gene-gene network, remarkable progress has been made in rigorous statistical inference of high-dimensional Gaussian graphical model (GGM). These approaches provide a formal confidence interval or a p-value rather than only a single point estimator for conditional dependence of a gene pair and are more desirable for identifying reliable gene networks. To promote their widespread use, we herein introduce an extensive and efficient R package named SILGGM (Statistical Inference of Large-scale Gauss

SUBMITTER: Zhang R 

PROVIDER: S-EPMC6107288 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature