BRGM: Learning gene regulatory networks for breast cancer.

In GGMM: Mixture Gaussian Graphical Models

Description

Gaussian Graphical Mixture Models for learning gene regulatory network with multiple subtypes of breat cancer dataset.

Usage

BRGM(data, M=3, alpha1 = 0.05, alpha2 = 0.02, alpha3 = 0.2, iteration = 30, warm = 20)

Arguments

data	A nxp matrix of breast cancer expression data.
M	The number of heterogeneous groups, default of 3 based on the BIC scores.
alpha1	The significance level of correlation screening in the ψ-learning algorithm, see R package equSA for detail. In general, a high significance level of correlation screening will lead to a slightly large separator set, which reduces the risk of missing important variables in the conditioning set. In general, including a few false variables in the conditioning set will not hurt much the accuracy of the ψ-partial correlation coefficient, the default value is 0.05.
alpha2	The significance level of ψ-partial correlation coefficient screening for estimating the adjacency matrix, see equSA, the default value is 0.02.
alpha3	The significance level of integrative ψ-partial correlation coefficient screening for estimating the adjacency matrix of GGMM method, the default value is 0.2.
iteration	The number of total iterations, the default value is 30.
warm	The number of burn-in iterations, the default value is 20.

Value

Adj	pxp Estimated adjacency matrix for network construction.
label	The estimated group indices for each observation.
BIC	The BIC scores for determining the number of groups M.

Author(s)

Bochao [e-mail] and Faming Liang

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.

Liang, F. and Zhang, J. (2008) Estimating FDR under general dependence using stochastic approximation. Biometrika, 95(4), 961-977.

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.

Examples

 


library(GGMM)
library(huge)
data(breast)
## long time ##
M <- 3
Gra <- BRGM(breast, M, iteration = 30, warm = 20)
## plot gene regulatory network by our estimated adjacency matrix.
huge.plot(Gra$Adj)





         

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