scHOT

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,
  ...
)

Arguments

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 BiocParallelParam class object from the BiocParallel package is used. Default is SerialParam().
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

Value

A scHOT object

Examples

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)
#> Adding testing scaffold 
#> Set weight matrix 
#> weightMatrix not provided, generating one using parameter settings...
#> Calculate gobal higher order function 
#> higherOrderFunctionType given will replace any stored param
#> higherOrderFunction given will replace any stored param
#> Calculate Higher Order Test Statistics 
#> numberScaffold set higher than the scaffold, setting permutation number for all tests
#> higherOrderSummaryFunction will replace any stored param
#> Perform Permutation Test 
#> Permutation testing combination 1 of 2...
#> Permutation testing combination 2 of 2...
#> Estimating p-values 
#> 1 
#> 2