IROmiss (version 1.0.2)

YeastIRO: Learning gene regulatory networks for Yeast Cell Expression Data.

Description

An Imputation Regularized Optimization (IRO) algorithm for learning gene regulatory networks with missing data. The dataset is collected from the yeast Saccharomyces cerevisiae responding to diverse environmental changes and is available at http://genome-www.stanford.edu/yeast-stress/.

Usage

YeastIRO(data, alpha1 = 0.05, alpha2 = 0.01, alpha3 = 0.01, iteration = 30, warm = 20)

Arguments

data

\(n\)xp Yeast Cell expression data.

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 0.01.

alpha3

The significance level of integrative \(\psi\)-partial correlation coefficient screening for estimating the adjacency matrix of IRO_Ave method, the default value is 0.01.

iteration

The number of total iterations, the default value is 30.

warm

The number of burn-in iterations, the default value is 20.

Value

A

pxp Estimated adjacency matrix for network construction.

%% ...

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 {
library(IROmiss)
library(huge)
data(yeast)
## long time ##
A <- YeastIRO(yeast, alpha1 = 0.05, alpha2 = 0.01, alpha3 = 0.01, iteration = 30, warm = 20)
## plot gene regulatory network by our estimated adjacency matrix.
huge.plot(A)

# }
# NOT RUN {


         
# }