scHOT_estimatePvalues {scHOT} | R Documentation |
Estimate p-values based on already run permutation tests
scHOT_estimatePvalues( scHOT, usenperm_estimate = FALSE, nperm_estimate = 10000, maxDist = 0.1, plot = FALSE, verbose = FALSE )
scHOT |
A scHOT object |
usenperm_estimate |
Logical (default FALSE) if number of neighbouring permutations should be used, 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 |
scHOT A scHOT object with results stored in scHOT_output slot
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_addTestingScaffold(scHOT_spatial, pairs) scHOT_spatial <- scHOT_setWeightMatrix(scHOT_spatial, positionColData = c("x","y"), positionType = "spatial", nrow.out = NULL, span = 0.05) scHOT_spatial <- scHOT_calculateGlobalHigherOrderFunction( scHOT_spatial, higherOrderFunction = weightedSpearman, higherOrderFunctionType = "weighted") scHOT_spatial <- scHOT_setPermutationScaffold(scHOT_spatial, numberPermutations = 100) scHOT_spatial <- scHOT_calculateHigherOrderTestStatistics( scHOT_spatial, higherOrderSummaryFunction = sd) scHOT_spatial <- scHOT_performPermutationTest( scHOT_spatial, verbose = TRUE, parallel = FALSE) scHOT_spatial <- scHOT_estimatePvalues(scHOT_spatial)