GeneClusterInterp: Interpolating gene expression measurements
In GeneClusterNet: Gene Expression Clustering and Gene Network

Description Usage Arguments Value Author(s) References Examples

To perform the dynamic Bayesian network analysis, the time points of gene expression measurements have to be evenly spaced. If the original time points in a dataset are not even, this function can not only interpolate measurements to have evenly spaced time course gene expressions, but also allow users to specify the number of time points.

1	GeneClusterInterp(LOPCoefficient, OriginalTime, outLen = 20)

`LOPCoefficient`	coefficient matrix of LOP.
`OriginalTime`	time vector
`outLen`	number of new time points.

A matrix with J + 1 rows ( J is number of Cluster) and outLen columns ( number of equal time space). The first row of it provides the new time vector.

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),]
 LOPCoefficient =GeneCluster (Sample, times=c(1:18), NumberOfCluster=3,orderLOP=5)$ LOPCoefficient
 GeneClusterInterp (LOPCoefficient, OriginalTime=c(1:18),outLen=20)