GeneClusterNet: Inference of gene regulatory network
In GeneClusterNet: Gene Expression Clustering and Gene Network

Description Usage Arguments Value Author(s) References Examples

This function clusters gene expressions and makes inference of gene regulatory network with dynamic Bayesian network method based on G1DBN. It integrates the analyses of GeneCluster, GeneClusterBIC, GeneClusterInterp and G1DBN.

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GeneClusterNet(mExpression, times, orderLOP, alpha1 = 0.5, alpha2 = 0.05, 
	realign = F, cutoff = c(lowCut = -0.35, upCut = 0.2), 
	NumberOfCluster = 0, sLabels = NULL)

`mExpression`	a gene expression matrix with p columns (length of time vector) and n rows ( number of genes).
`times`	time vector
`orderLOP`	order of Legendre Polynomials
`alpha1`	threshold that use for edge selection in the 1st order dependence score matrix S1 and its default setting is 0.5. See https://cran.r-project.org/web/packages/G1DBN/G1DBN.pdf.
`alpha2`	threshold that use for edge selection in the score matrix S2 and its default setting is 0.05. See https://cran.r-project.org/web/packages/G1DBN/G1DBN.pdf
`realign`	defalut is FALSE, realign is true, the cutoff variable indicates a cutoff range
`cutoff`	cutoff range for determining the time of initial up or down regulaiotn.
`NumberOfCluster`	number of cluster. if it is set to zero, the function will determine the optimal number of gene expression clusters automatically.
`sLabels`	defalut is NULL, the user could specify the cluster labels when number of cluster is specified.

A score matrix and an adjacency matrix. It also creates a list of plots same as the function GeneCluster. In addtition, it creates a plot of gene regulatory network.

Yaqun Wang yw505@sph.rutgers.edu, Zhengyang Shi

Wang, Y., Xu, M., Wang, Z., Tao, M., Zhu, J., Wang, L., et al. (2012). How to cluster gene expression dynamics in response to environmental signals. Briefings in bioinformatics, 13(2), 162-174.

Wang, Y., Berceli, S. A., Garbey, M. and Wu, R. (2016). Inference of gene regulatory network through adaptive dynamic Beyesian networm modeling. Technical Report.

R package G1DBN available at https://cran.r-project.org/package=G1DBN

 # load the package 
 library(GeneClusterNet)
 set.seed(1234)
 data(mExpression)
 Sample=mExpression[sample(1:nrow(mExpression),50,replace=FALSE),]
 GeneClusterNet (Sample, times=c(1:18), orderLOP=5 ,alpha1=0.5, alpha2=0.05,NumberOfCluster = 3)