# ---------------------------------------------------------------------------- #

#                              Data pre-processing                             #

# ---------------------------------------------------------------------------- #

context("Data loading and pre-processing")

sc <- DISCBIO(valuesG1msTest) # Reduced dataset used for testing

test_that("Loading datasets generate the expected output", {

  expect_equal(dim(valuesG1msTest), c(800, 15))

})

test_that("Data signature changes", {

  expect_equal(class(sc)[1], "DISCBIO")

  expect_equal(attr(class(sc), "package"), "DIscBIO")

})

# This function will be used only if the dataset has ERCC

sc <- NoiseFiltering(sc, plot = FALSE, export = FALSE, quiet = TRUE)

test_that("Noise filtering is added", {

  expect_equal(length(sc@noiseF), 163)

})

# In this case this function is used to normalize the reads

sc <- Normalizedata(

  sc,

  mintotal = 1000, minexpr = 0, minnumber = 0, maxexpr = Inf,

  downsample = FALSE, dsn = 1, rseed = 17000

)

test_that("Data is normalized", {

  expect_equal(class(sc@fdata), "data.frame")

  expect_output(str(sc@fdata), "708 obs. of  15 variables")

})

# This function can be used for:

# 1 - filtering and normalizing the dataset that has no ERCC.

# 2 - to normalize and filter genes and cells after the noise filtering.

sc <- FinalPreprocessing(

  sc,

  GeneFlitering = "NoiseF", export = FALSE, quiet = TRUE

)

test_that("Data is normalized", {

  expect_equal(dim(sc@fdata), c(163, 15))

})

# ---------------------------------------------------------------------------- #

#                              K-means clustering                              #

# ---------------------------------------------------------------------------- #

context("K-means clustering")

sc <- Clustexp(

  sc,

  clustnr = 2, cln = 2, bootnr = 10, quiet = TRUE, rseed = 17000

)

sc <- comptSNE(sc, rseed = 15555, quiet = TRUE, max_iter = 1, epoch = 10)

test_that("tSNE is computed", {

  expect_equal(class(sc@tsne), "data.frame")

  expect_output(str(sc@tsne), "15 obs. of  2 variables")

})

test_that("Cluster plots output is as expexted", {

  expect_equivalent(

    object = Jaccard(sc, Clustering = "K-means", K = 2, plot = FALSE, R = 5),

    expected = c(.683, .624),

    tolerance = .01

  )

  expect_equal(

    object = clustheatmap(sc, hmethod = "single", plot = FALSE),

    expected = c(1, 2)

  )

})

# --------------------------------- Outliers --------------------------------- #

context("Outliers")

Outliers <- FindOutliers(

  sc,

  K = 2, outminc = 5, outlg = 2, probthr = .5 * 1e-3, thr = 2^(-1:-40),

  outdistquant = .75, plot = FALSE, quiet = TRUE

)

Order <- pseudoTimeOrdering(sc, quiet = TRUE, export = FALSE)

test_that("Outliers are the expected", {

  expect_equivalent(Outliers, c(3, 10, 13))

  expect_equivalent(

    object = Order@kordering,

    expected = c(

      5, 6, 13, 2, 3, 1, 4, 7, 12, 9, 15, 8, 10, 11, 14

    )

  )

})