GeneClusterNet (version 1.0.1)

GeneClusterBIC: Optimal number of Gene Clusters

Description

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.

Usage

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

Arguments

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

Value

A plot shows the smallest BIC.

References

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.

Examples

 # 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)