Quantify the similarity of gene co-expression between a reference and a test dataset. Statistical significance is calculated using permutation of the genes.

calcCCD(
refCor,
emat,
groupVec = NULL,
refEmat = NULL,
nPerm = 1000,
geneNames = NULL,
dopar = FALSE
)

## Arguments

refCor Correlation matrix to be used as the reference, such as comes from getRefCor(). Should contain Spearman correlation values. Matrix of expression values, where each row corresponds to a gene and each column corresponds to a sample. The rownames and colnames of refCor should be present in the rownames of emat. For the p-value calculation, it is important that emat include all measured genes, not just those in refCor. Optional vector indicating the group to which group each sample belongs. If not provided, the function assumes all samples belong to the same group. Optional expression matrix for calculating co-expression for the reference, with the same organization as emat. Only used if refCor is not provided. Number of permutations for assessing statistical significance. Optional vector indicating a subset of genes in refCor, emat, and/or refEmat to use for calculating the CCD. Logical indicating whether to process features in parallel. Make sure to register a parallel backend first.

## Value

A data frame with columns for group name, CCD, and p-value.

getRefCor(), calcDeltaCCD(), plotHeatmap()

## Examples

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,
'non-tumor', dopar = TRUE)

pRef = plotRefHeatmap(refCor)
pTest = plotHeatmap(rownames(refCor), GSE19188$emat, GSE19188$groupVec)
}