scHOT {scHOT} | R Documentation |
A wrapper function to perform scHOT
scHOT( scHOT, testingScaffold = NULL, weightMatrix = NULL, positionType = NULL, positionColData = NULL, nrow.out = NULL, averageAcrossTrajectoryTies = FALSE, higherOrderFunction = NULL, higherOrderFunctionType = NULL, numberPermutations = 1000, numberScaffold = 100, storePermutations = TRUE, higherOrderSummaryFunction = sd, parallel = FALSE, BPPARAM = BiocParallel::SerialParam(), usenperm_estimate = FALSE, nperm_estimate = 10000, maxDist = 0.1, plot = FALSE, verbose = TRUE, ... )
scHOT |
A scHOT object |
testingScaffold |
A matrix with rows for each testing combination |
weightMatrix |
A matrix indicates the weight matrix for scHOT analysis |
positionType |
A string indicating the position type, either "trajectory" or "spatial" |
positionColData |
Either trajectory or spatial information for each sample. If positionType is "trajectory" then positionColData should be a character or numeric indicating the subset of colData of the scHOT object. If positionType is "spatial" then positionColData should be a character or numeric vector indicating the subset of colData that give the full spatial coordinates. |
nrow.out |
The number of weightings to include for testing, a smaller value is faster for computation |
averageAcrossTrajectoryTies |
Logical indicating whether ties in the trajectory should be given the same local weights |
higherOrderFunction |
A function object indicates the higher order function |
higherOrderFunctionType |
is "weighted" or "unweighted", determines if there is a weighting argument in the higher order function |
numberPermutations |
The number of permutations, set as 1000 by default |
numberScaffold |
The number of testing scaffolds to perform permutations, set as 100 by default |
storePermutations |
a logical flag on whether permutation values should be saved |
higherOrderSummaryFunction |
A functon indicating the higher order summary function (default is standard deviation 'sd') |
parallel |
A logical input indicating whether to run the permutation test using multiple cores in parallel. |
BPPARAM |
A |
usenperm_estimate |
Logical (default FALSE) if number of neighbouring permutations should be used to estimate P-values, or if difference of global higher order statistic should be used |
nperm_estimate |
Number of neighbouring permutations to use for p-value estimation |
maxDist |
max difference of global higher order statistic to use for p-value estimation (default 0.1) |
plot |
A logical input indicating whether the results are plotted |
verbose |
A logical input indicating whether the intermediate steps will be printed |
... |
parameters for function trajectoryWeightMatrix or spatialWeightMatrix |
A scHOT object
data(MOB_subset) sce_MOB_subset <- MOB_subset$sce_MOB_subset scHOT_spatial <- scHOT_buildFromSCE(sce_MOB_subset, assayName = "logcounts", positionType = "spatial", positionColData = c("x", "y")) pairs <- matrix(c("Arrb1", "Mtor", "Dnm1l", "Gucy1b3"), ncol = 2, byrow = TRUE) rownames(pairs) <- apply(pairs,1,paste0,collapse = "_") scHOT_spatial <- scHOT(scHOT_spatial, testingScaffold = pairs, positionType = "spatial", positionColData = c("x", "y"), nrow.out = NULL, higherOrderFunction = weightedSpearman, higherOrderFunctionType = "weighted", numberPermutations = 100, higherOrderSummaryFunction = sd, parallel = FALSE, verbose = TRUE, span = 0.05)