GeneClusterBIC: Optimal number of Gene Clusters
In GeneClusterNet: Gene Expression Clustering and Gene Network

Description Usage Arguments Value Author(s) References Examples

Given time cousre expressions of n genes, time vector, order of Legendre Polynomials and a range of cluster numbers, e.g. from 1 to 15, the function can identify the optimal number of clusters, which has the smallest BIC value.

1	GeneClusterBIC(mExpression, times, G = c(1:15), orderLOP)

`mExpression`	a gene expression matrix with p columns (length of time vector) and n rows ( number of genes).
`times`	time vector specifies the time points of measurements.
`G`	range of number of clusters
`orderLOP`	order of Legendre Polynomials

A list of BIC corresponds to every number of clusters and the optimal BIC. A plot shows the smallest BIC.

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.

 # load the package 
 library(GeneClusterNet)
 set.seed(1234)
 data(mExpression)
 Sample=mExpression[sample(1:nrow(mExpression),50,replace=FALSE),]
 GeneClusterBIC(Sample, times=c(1:18), G=c(1:5), orderLOP=5)