GeneClusterNet (version 1.0.1)

GeneClusterInterp: Interpolating gene expression measurements

Description

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.

Usage

GeneClusterInterp(LOPCoefficient, OriginalTime, outLen = 20)

Arguments

LOPCoefficient

coefficient matrix of LOP.

OriginalTime

time vector

outLen

number of new time points.

Value

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.

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