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

In IROmiss: Imputation Regularized Optimization Algorithm

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	nxp Yeast Cell expression data.
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 0.01.
alpha3	The significance level of integrative ψ-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.

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(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)





         

IROmiss documentation built on March 26, 2020, 5:56 p.m.