calcDeltaCCD {deltaccd} | R Documentation |
Calculate delta clock correlation distance.
Description
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.
Usage
calcDeltaCCD(
refCor,
emat,
groupVec,
groupNormal,
refEmat = NULL,
nPerm = 1000,
geneNames = NULL,
dopar = FALSE,
scale = FALSE
)
Arguments
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 co-expression 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. Make sure to register a parallel backend first. |
scale |
Logical indicating whether to use scaled CCDs to calculate difference. |
Value
A data.table with columns for group 1, group 2, deltaCCD, and
p-value. In each row, the deltaCCD is the CCD of group 2 minus the CCD of
group 1, so group 1 corresponds to groupNormal
.
See Also
getRefCor()
, calcCCD()
, plotHeatmap()
Examples
set.seed(35813)
refCor = getRefCor()
deltaCcdResult = calcDeltaCCD(
refCor, GSE19188$emat, GSE19188$groupVec, 'healthy', nPerm = 100)