GeneCluster: Clustering dynamic gene expression
In GeneClusterNet: Gene Expression Clustering and Gene Network

Description Usage Arguments Value Author(s) References Examples

Providing functional clustering of time course gene expressions by using the Legendre orthogonal polynomials (LOP) to model cluster-specific curves for each cluster

1	GeneCluster(mExpression, times, NumberOfCluster, 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.
`NumberOfCluster`	number of cluster (J)
`orderLOP`	order of Legendre Polynomials (r)

A list that contains $MeanExpression is the matrix with J rows ( J is number of Cluster) and n columns ( length of time vector), each rows is the mean expression of a cluster. $LOPCoefficient is the coefficient matrix of LOP with J rows and r+1 columns.$Classifications indicates the cluster label for each of genes. A list of Plots, first plot is the mean expression plot for every clusters and each of the rest plots displays the trajectories of gene expressions in each cluster.

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 the number of Cluster is 3 and order of Legendre Polynomials is 5.
 set.seed(1234)
 data(mExpression)
 
 Sample=mExpression[sample(1:nrow(mExpression),50,replace=FALSE),]

 GeneCluster(Sample, times=c(1:18), NumberOfCluster=3,orderLOP=5)