calcDeltaCCD | R Documentation |
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, scale = 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 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. |
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
.
getRefCor()
, calcCCD()
, plotHeatmap()
set.seed(35813) refCor = getRefCor() deltaCcdResult = calcDeltaCCD( refCor, GSE19188$emat, GSE19188$groupVec, 'healthy', nPerm = 100)
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