Nothing
data(hgsc)
hgsc <- hgsc[1:40, 1:30]
test_that("No algorithms means all algorithms, output is an array", {
skip_if_not_installed("apcluster")
skip_if_not_installed("blockcluster")
skip_if_not_installed("cluster")
skip_if_not_installed("dbscan")
skip_if_not_installed("e1071")
skip_if_not_installed("kernlab")
skip_if_not_installed("kohonen")
x1 <- consensus_cluster(hgsc, nk = 4, reps = 1, progress = FALSE)
expect_error(x1, NA)
expect_is(x1, "array")
})
test_that("Output can be saved with or without time in file name", {
x1 <- consensus_cluster(hgsc, nk = 2:4, reps = 5, algorithms = "hc",
progress = FALSE, file.name = "CCOutput")
x2 <- consensus_cluster(hgsc, nk = 2:4, reps = 5, algorithms = "hc",
progress = FALSE, file.name = "CCOutput",
time.saved = TRUE)
expect_identical(x1, x2)
file.remove(list.files(pattern = "CCOutput"))
})
test_that("Progress bar increments across entire function call", {
skip_if_not_installed("apcluster")
assign("my_dist", function(x) stats::dist(x, method = "manhattan"), pos = 1)
x3 <- consensus_cluster(hgsc, nk = 2, reps = 5,
algorithms = c("nmf", "hc", "ap"),
distance = c("spear", "my_dist"), nmf.method = "lee",
progress = TRUE)
expect_error(x3, NA)
})
test_that("Able to call only spearman distance", {
x4a <- consensus_cluster(hgsc, nk = 2, reps = 5, algorithms = "hc",
distance = "spear")
expect_error(x4a, NA)
x4b <- consensus_cluster(hgsc, nk = 2, reps = 5, algorithms = "hc",
distance = "spear", abs = FALSE)
expect_error(x4b, NA)
})
test_that("Data preparation on bootstrap samples works", {
skip_if_not_installed("apcluster")
x5 <- consensus_cluster(hgsc, nk = 3, reps = 3,
algorithms = c("nmf", "hc", "ap"), nmf.method = "lee",
prep.data = "sampled")
expect_error(x5, NA)
})
test_that("no scaling means only choose complete cases and high signal vars", {
x6 <- consensus_cluster(hgsc, nk = 2, reps = 2, algorithms = "hc",
scale = FALSE)
expect_error(x6, NA)
})
test_that("t-SNE dimension reduction works", {
x7 <- consensus_cluster(hgsc, nk = 4, reps = 1, algorithms = c("hc", "km"),
type = "tsne")
expect_error(x7, NA)
})
test_that("Able to call Pearson distance", {
x8 <- consensus_cluster(hgsc, nk = 2, reps = 5, algorithms = "hc",
distance = "pearson")
expect_error(x8, NA)
})
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.