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

## Usage

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

## Arguments

- refCor
Correlation matrix to be used as the reference, such as comes from

`getRefCor()`

. Should contain Spearman correlation values.- emat
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`

.- groupVec
Optional vector indicating the group to which group each sample belongs. If not provided, the function assumes all samples belong to the same group.

- refEmat
Optional expression matrix for calculating co-expression for the reference, with the same organization as

`emat`

. Only used if`refCor`

is not provided.- nPerm
Number of permutations for assessing statistical significance.

- geneNames
Optional vector indicating a subset of genes in

`refCor`

,`emat`

, and/or`refEmat`

to use for calculating the CCD.- dopar
Logical indicating whether to process features in parallel. Make sure to register a parallel backend first.

- scale
Logical indicating whether to scale CCD by the number of gene pairs.