if (interactive()) {
  # ======================================================== #
  # Loading and rearranging files                            #
  # ======================================================== #

  notebook_data_path <- system.file("notebook", package = "DIscBIO")
  load(file.path(notebook_data_path, "SC.RData"))
  load(file.path(notebook_data_path, "Ndata.RData"))
  load(file.path(notebook_data_path, "expdata.RData"))
  load(file.path(notebook_data_path, "DATAforDT.RData"))

  sc <- SC
  sc@ndata <- Ndata
  sc@expdata <- expdata

  rm(Ndata)
  rm(expdata)
  rm(SC)

  # ======================================================== #
  # differential expression analysis                         #
  # ======================================================== #

  context("Binder tests, part 2: Differential expression analysis")

  cdiff <- DEGanalysis2clust(sc, 4, quiet = TRUE, plot = FALSE)

  test_that("DEG analysis between 2 clusters", {
    expect_equal(
      object = head(cdiff[[1]])[, 1],
      expected = c(
        "ENSG00000008988",  "ENSG00000010278",  "ENSG00000034510",
        "ENSG00000071082",  "ENSG00000071127",  "ENSG00000075624"
      )
    )
    expect_equal(
      object = head(cdiff[[1]])[, 2],
      expected = c("RPS20", "CD9", "TMSB10", "RPL31", "WDR1", "ACTB")
    )
    expect_equivalent(
      object = as.character(head(cdiff[[2]])[1, ]),
      expected = c(
        "CL1 VS CL2", "CL2", "106", "Up-regulated-NameCL2inCL1VSCL2.csv",
        "82", "Low-regulated-NameCL2inCL1VSCL2.csv"
      )
    )
    expect_equal(
      object = as.character(head(cdiff[[2]])[2, ]),
      expected = c(
        "CL1 VS CL2", "CL1", "106", "Low-regulated-NameCL1inCL1VSCL2.csv",
        "82", "Up-regulated-NameCL1inCL1VSCL2.csv"
      )
    )
  })

  cdiffBinomial <- ClustDiffGenes(sc, 4, quiet = TRUE)

  test_that("Cluster differences", {
    expect_equal(
      object = head(cdiffBinomial[[1]])[, 1],
      expected = c(
        "ENSG00000001630",  "ENSG00000002586",  "ENSG00000003402",
        "ENSG00000003436",  "ENSG00000003756",  "ENSG00000004059"
      )
    )
    expect_equal(
      object = head(cdiffBinomial[[1]])[, 2],
      expected = c("CYP51A1", "CD99", "CFLAR", "TFPI", "RBM5", "ARF5")
    )
    expect_equivalent(
      object = as.character(head(cdiffBinomial[[2]])[1, ]),
      expected = c(
        "Cluster 1", "Remaining Clusters", "1052", "Up-DEG-cluster1.csv",
        "678", "Down-DEG-cluster1.csv"
      )
    )
    expect_equal(
      object = as.character(head(cdiffBinomial[[2]])[2, ]),
      expected = c(
        "Cluster 2", "Remaining Clusters", "0", "Up-DEG-cluster2.csv", "1",
        "Down-DEG-cluster2.csv"
      )
    )
  })


  # ======================================================== #
  # Decision trees                                           #
  # ======================================================== #

  context("Binder tests, part 2: Decision trees")

  j48dt <- J48DT(DATAforDT, plot = FALSE, quiet = TRUE)
  rpartDT <- RpartDT(DATAforDT, plot = FALSE, quiet = TRUE)

  test_that("J48 trees", {
    expect_true(is(summary(j48dt), "Weka_classifier_evaluation"))
    expect_output(str(rpartDT), "List of 14")
  })

  # ======================================================== #
  # Networking                                               #
  # ======================================================== #

  context("Binder tests, part 2: Networking")

  data <- cdiffBinomial[[1]][1:200, 2] # only the firat 200 genes
  ppi <- suppressMessages(PPI(data))
  networking <- suppressMessages(NetAnalysis(ppi))

  test_that("Networks", {
    expect_gt(nrow(ppi), 1000)
    expect_equal(ncol(ppi), 13)
    expect_gt(nrow(networking), 150)
    expect_equal(ncol(networking), 3)
  })
}