GGMM-package: Gaussian Graphical Mixture Models

In GGMM: Mixture Gaussian Graphical Models

Description

The Gaussian graphical model is a widely used tool for learning gene regulatory networks with high-dimensional gene expression data. For many real problems, the data are heterogeneous, which may contain some subgroups or come from different resources. This package provide a Gaussian Graphical Mixture Model (GGMM) for the heterogeneous data.

Details

Package:	GGMM
Type:	Package
Version:	1.0.1
Date:	2019-03-17
License:	GPL-2

This package illustrates the use of the Gaussian Graphical Mixture Model in two parts:

The first part is to apply the GGMM to estimate network structures using high-dimensional heterogeneous data with a simulated dataset SimHetDat(n,p,...) and our proposed method GGMM(data,...).

The second part is to apply the GGMM to learn a real data example BRGM(breast,...), i.e. to learn a common gene regulatory network with heterogeneous gene expression data of breast cancer. The real data example are from The Cancer Genome Atlas (TCGA) with code data(breast).

Author(s)

Bochao Jia, Faming Liang Maintainer: Bochao Jia<[e-mail]>

References

Liang, F., Song, Q. and Qiu, P. (2015). An Equivalent Measure of Partial Correlation Coefficients for High Dimensional Gaussian Graphical Models. J. Amer. Statist. Assoc., 110, 1248-1265.<doi:10.1080/01621459.2015.1012391>

Liang, F. and Zhang, J. (2008) Estimating FDR under general dependence using stochastic approximation. Biometrika, 95(4), 961-977.<doi:10.1093/biomet/asn036>

Liang, F., Jia, B., Xue, J., Li, Q., and Luo, Y. (2018). An Imputation Regularized Optimization Algorithm for High-Dimensional Missing Data Problems and Beyond. Submitted to Journal of the Royal Statistical Society Series B. <arXiv:1802.02251>

Jia, B., Xu, S., Xiao, G., Lamba, V., Liang, F. (2017) Inference of Genetic Networks from Next Generation Sequencing Data. Biometrics.

Jia, B. and Liang, F. (2018). Learning Gene Regulatory Networks with High-Dimensional Heterogeneous Data. Accept by ICSA Springer Book. <arXiv:1805.02547>

Examples

 

library(GGMM)
library(huge)
result <- SimHetDat(n = 100, p = 200, M = 2, mu = 0.5, type = "band")
Graph <- GGMM(result$data, result$A, M = 2, iteration = 30, warm = 20)
      

GGMM documentation built on May 1, 2019, 9:36 p.m.