GeneClusterNet (version 1.0.1)

GeneCluster: Clustering dynamic gene expression

Description

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

Usage

GeneCluster(mExpression, times, NumberOfCluster, 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.

NumberOfCluster

number of cluster (J)

orderLOP

order of Legendre Polynomials (r)

Value

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.

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 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)