Internal .evalDiffCorr
Usage
.evalDiffCorr(
epiSignal,
testVariable,
gRanges,
clustList,
npermute = c(100, 10000, 1e+05),
adj.beta = 0,
rho = 0,
sumabs.seq = 1,
BPPARAM = bpparam(),
method = c("sLED", "Box", "Box.permute", "Steiger.fisher", "Steiger", "Jennrich",
"Factor", "Mann.Whitney", "Kruskal.Wallis", "Cai.max", "Chang.maxBoot", "LC.U",
"WL.randProj", "Schott.Frob", "Delaneau", "deltaSLE"),
method.corr = c("pearson", "kendall", "spearman")
)Arguments
- epiSignal
matrix or EList of epigentic signal. Rows are features and columns are samples
- testVariable
factor indicating two subsets of the samples to compare
- gRanges
GenomciRanges corresponding to the rows of epiSignal
- clustList
list of cluster assignments
- npermute
array of two entries with min and max number of permutations
- adj.beta
parameter for sLED
- rho
a large positive constant such that A(X)-A(Y)+diag(rep(rho,p)) is positive definite. Where p is the number of features
- sumabs.seq
sparsity parameter
- BPPARAM
parameters for parallel evaluation
- method
"sLED", "Box", "Box.permute", "Steiger.fisher", "Steiger", "Jennrich", "Factor", "Mann.Whitney", "Kruskal.Wallis", "Cai.max", "Chang.maxBoot", "LC.U", "WL.randProj", "Schott.Frob", "Delaneau", "deltaSLE"
- method.corr
Specify type of correlation: "pearson", "kendall", "spearman"