Calculate the difference between the clock correlation distances (CCDs), relative to a reference, for two groups of samples. Statistical significance is calculated using permutation of the samples that belong to either of those two groups.
calcDeltaCCD( refCor, emat, groupVec, groupNormal, refEmat = NULL, nPerm = 1000, geneNames = NULL, dopar = FALSE )
refCor  Correlation matrix to be used as the reference, such as comes
from 

emat  Matrix of expression values, where each row corresponds to a gene
and each column corresponds to a sample. The rownames and colnames of

groupVec  Vector indicating the group to which group each sample belongs. It's ok for groupVec to have more than two groups. 
groupNormal  Value indicating the group in groupVec that corresponds to normal or healthy. Other groups will be compared to this group. 
refEmat  Optional expression matrix for calculating coexpression for
the reference, with the same organization as 
nPerm  Number of permutations for assessing statistical significance. 
geneNames  Optional vector indicating a subset of genes in 
dopar  Logical indicating whether to process features in parallel. Use

A data frame with columns for group 1, group 2, deltaCCD, and
pvalue. In each row, the deltaCCD is the CCD of group 2 minus the CCD of
group 1, so group 1 corresponds to groupNormal
.
if (FALSE) { library('deltaccd') library('doParallel') library('doRNG') registerDoParallel(cores = 2) set.seed(35813) refCor = getRefCor() ccdResult = calcCCD(refCor, GSE19188$emat, GSE19188$groupVec, dopar = TRUE) deltaCcdResult = calcDeltaCCD(refCor, GSE19188$emat, GSE19188$groupVec, 'nontumor', dopar = TRUE) pRef = plotRefHeatmap(refCor) pTest = plotHeatmap(rownames(refCor), GSE19188$emat, GSE19188$groupVec) }