Nothing
test_that("scDHA can run", {
skip_on_os("windows")
skip_on_cran()
#Load example data (Goolam dataset)
data('Goolam'); data <- t(Goolam$data); label <- as.character(Goolam$label)
#Log transform the data
data <- log2(data + 1)
#Generate clustering result, the input matrix has rows as samples and columns as genes
result <- scDHA(data, ncores = 2, seed = 1)
expect_is(result, 'list')
expect_is(result$cluster, 'numeric')
expect_equal(length(result$cluster), nrow(data))
})
test_that("scDHA.vis can run", {
#Load preprocessing data
data("Goolam_result")
result <- Goolam_result
#Generate 2D representation, the input is the output from scDHA function
result <- scDHA.vis(result, ncores = 2, seed = 1)
expect_is(result, 'list')
expect_is(result$pred, 'matrix')
expect_equal(length(result$pred), length(result$cluster)*2)
})
test_that("scDHA.pt can run", {
#Load preprocessing data
data("Goolam_result")
result <- Goolam_result
#Generate pseudo-time for each cell, the input is the output from scDHA function
result <- scDHA.pt(result, start.point = 1, ncores = 2, seed = 1)
expect_is(result, 'list')
expect_is(result$pt, 'numeric')
expect_equal(length(result$pt), length(result$cluster))
})
test_that("scDHA.class can run", {
skip_on_os("windows")
skip_on_cran()
#Load example data (Goolam dataset)
data('Goolam'); data <- t(Goolam$data); label <- as.character(Goolam$label)
#Log transform the data
data <- log2(data + 1)
#Split data into training and testing sets
set.seed(1)
idx <- sample.int(nrow(data), size = round(nrow(data)*0.75))
train.x <- data[idx, ]; train.y <- label[idx]
test.x <- data[-idx, ]; test.y <- label[-idx]
#Predict the labels of cells in testing set
prediction <- scDHA.class(train = train.x, train.label = train.y, test = test.x,
ncores = 2, seed = 1)
expect_is(prediction, 'character')
expect_equal(length(prediction), nrow(test.x))
})
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