View source: R/scHOT_functions.R
scHOT_estimatePvalues | 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)
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