GGMM (version 1.0.1)

BRGM: Learning gene regulatory networks for breast cancer.

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 \(n\)x\(p\) 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 \(\psi\)-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 \(\psi\)-partial correlation coefficient, the default value is 0.05.

alpha2

The significance level of \(\psi\)-partial correlation coefficient screening for estimating the adjacency matrix, see equSA, the default value is 0.02.

alpha3

The significance level of integrative \(\psi\)-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

\(p\)x\(p\) Estimated adjacency matrix for network construction.

label

The estimated group indices for each observation.

BIC

The BIC scores for determining the number of groups \(M\).

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

# NOT RUN {
 
# }
# NOT RUN {
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)

# }
# NOT RUN {


         
# }