calcCCD: Calculate clock correlation distance (CCD).

In hugheylab/deltaccd: Quantify Rhythmic Gene Co-Expression Relative to a Reference

Calculate clock correlation distance (CCD).

Description

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.

Value

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

See Also

getRefCor(), calcDeltaCCD(), plotHeatmap()

Examples

set.seed(35813)

refCor = getRefCor()
ccdResult = calcCCD(refCor, GSE19188$emat, GSE19188$groupVec, nPerm = 100)

hugheylab/deltaccd documentation built on Dec. 3, 2022, 9:51 a.m.