COTAN: COexpression Tables ANalysis

library(Matrix)

tm <- tempdir()
stopifnot(file.exists(tm))

test_that("Logging", {
  logPath <- file.path(tm, "COTAN_Test.log")

  suppressMessages({
    currentLevel <- setLoggingLevel(0L)
  })
  suppressMessages({
    currentFile  <- setLoggingFile(logPath)
  })

  expect_false(is.null(currentLevel[[1L]]))
  expect_null(currentFile[[1L]])
  expect_true(file.exists(logPath))

  expect_no_message(suppressMessages(logThis("This should not appear",
                                             logLevel = 0L, appendLF = FALSE)))

  expect_message(   logThis("This should appear",     logLevel = 0L))
  expect_no_message(logThis("This should not appear", logLevel = 1L))

  suppressMessages(setLoggingLevel(1L))

  expect_message(   logThis("This should appear",     logLevel = 1L))
  expect_no_message(logThis("This should not appear"))

  suppressMessages(setLoggingLevel(3L))
  suppressMessages(setLoggingFile(logPath))

  expect_message(logThis("This should appear",        logLevel = 3L))

  # restore logging status
  suppressMessages(setLoggingLevel(currentLevel))
  suppressMessages(setLoggingFile(""))

  expect_equal(R.utils::countLines(logPath), 5L, ignore_attr = TRUE)
  file.remove(logPath)
})


test_that("Clusterizations manipulations", {
  set.seed(1675787192L)
  clusters <- paste0("", as.roman(sample(7L, 100L, replace = TRUE)))

  elemNames <- paste0("el_", 1L:100L)
  clusters <- set_names(clusters, elemNames)

  clustersList <- toClustersList(clusters)

  expect_length(clustersList, 7L)
  expect_setequal(lengths(clustersList), as.vector(table(clusters)))

  clusters2 <- fromClustersList(clustersList, elemNames)

  expect_identical(clusters2, factor(clusters))

  clusters3 <- fromClustersList(clustersList, elemNames = NULL)
  expect_equal(table(clusters2), table(clusters3), ignore_attr = TRUE)

  positions <- groupByClusters(clusters)

  expect_identical(clusters2[positions], clusters3)

  expect_identical(groupByClusters(clusters2), positions)
  expect_identical(groupByClustersList(elemNames, clustersList), positions)

  # cause mismatches between the element names and the clusterization
  elemNames <- append(elemNames, paste0("el_", 201L:210L), after = 20L)[1L:100L]

  expect_setequal(fromClustersList(clustersList, elemNames)[21L:30L], "-1")

  expect_identical(groupByClustersList(elemNames, clustersList)[91L:100L],
                   (21L:30L))

  clusterM1 <- mergeClusters(clusters, names = as.roman(c(5L, 1L)),
                             mergedName = "I'V")

  expect_identical(levels(clusterM1)[[1L]], "I'V")
  expect_identical(table(clusterM1)[[1L]], sum(table(clusters)[c(1L, 5L)]))

  clusterM2 <-
    multiMergeClusters(clusters3, namesList = list(as.roman(c(1L, 5L)),
                                                   as.roman(c(6L, 2L, 4L))))

  expect_setequal(levels(clusterM2),
                  c("I_V-merge", "II_IV_VI-merge", "III", "VII"))
  expect_identical(sum(clusterM2 == "I_V-merge"), sum(clusterM1 == "I'V"))

  niceClusters <- niceFactorLevels(clusters)
  expect_identical(max(nchar(factorToVector(niceClusters))), 3L)
  expect_identical(min(nchar(factorToVector(niceClusters))), 3L)
  expect_true(all(endsWith(factorToVector(niceClusters), clusters)))

  levels(niceClusters) <- c(1L:3L, 11L:13L, 100L)
  niceClusters <- niceFactorLevels(niceClusters)
  expect_identical(max(nchar(factorToVector(niceClusters))), 3L)
  expect_identical(min(nchar(factorToVector(niceClusters))), 3L)
  expect_setequal(as.integer(levels(niceClusters)), c(1L:3L, 11L:13L, 100L))
})


test_that("Adding columns to data.frames", {
  df <- data.frame()

  df <- setColumnInDF(df, colName = "constant", colToSet = rep(1L, 10L))
  expect_identical(dim(df), c(10L, 1L))
  expect_setequal(df[["constant"]], 1L)

  df <- setColumnInDF(df, colToSet = (1L:10L),
                      colName = "sequence", rowNames = LETTERS[1L:10L])
  expect_identical(rownames(df), LETTERS[1L:10L])
  expect_identical(colnames(df), c("constant", "sequence"))

  df <- setColumnInDF(df, colName = "constant", colToSet = rep(2L, 10L))
  expect_identical(colnames(df), c("constant", "sequence"))
  expect_setequal(df[["constant"]], 2L)
})


test_that("funProbZero", {
  # Cases with mu = 0 are not actually in use
  expect_identical(funProbZero(-Inf, 0.0), NaN)
  expect_identical(funProbZero(-1.0, 0.0), 1.0)
  expect_identical(funProbZero( 0.0, 0.0), 1.0)
  expect_identical(funProbZero( 1.0, 0.0), 1.0)
  expect_identical(funProbZero(10.0, 0.0), 1.0)
  expect_identical(funProbZero( Inf, 0.0), NaN)

  # Cases with infinite disp can happen
  expect_identical(funProbZero(-Inf, 1.0),                0.0)
  expect_identical(funProbZero(-1.0, 1.0),          exp(-2.0))
  expect_identical(funProbZero( 0.0, 1.0),          exp(-1.0))
  expect_identical(funProbZero( 1.0, 1.0),          1.0 / 2.0)
  expect_identical(funProbZero(10.0, 1.0), 11.0^(-1.0 / 10.0))
  expect_identical(funProbZero( Inf, 1.0),                1.0)

  # Cases with mu = Inf are not actually in use
  expect_identical(funProbZero(-Inf, Inf), 0.0)
  expect_identical(funProbZero(-1.0, Inf), 0.0)
  expect_identical(funProbZero( 0.0, Inf), NaN)
  expect_identical(funProbZero( 1.0, Inf), 0.0)
  expect_identical(funProbZero(10.0, Inf), 0.0)
  expect_identical(funProbZero( Inf, Inf), 1.0)
})


test_that("funProbZero with matrices", {
  mu <- matrix((1L:25L) / 7.0, nrow = 10L, ncol = 10L)
  disp <- (-1L:8L) / 3.0

  p <- funProbZero(disp, mu)

  expect_identical(dim(p), dim(mu))

  expect_identical(p[ 1L,  1L], funProbZero(disp[[ 1L]], mu[ 1L,  1L]))
  expect_identical(p[ 1L, 10L], funProbZero(disp[[ 1L]], mu[ 1L, 10L]))

  expect_identical(p[ 3L,  7L], funProbZero(disp[[ 3L]], mu[ 3L,  7L]))
  expect_identical(p[ 6L,  4L], funProbZero(disp[[ 6L]], mu[ 6L,  4L]))

  expect_identical(p[10L,  1L], funProbZero(disp[[10L]], mu[10L,  1L]))
  expect_identical(p[10L, 10L], funProbZero(disp[[10L]], mu[10L, 10L]))
})


test_that("dispersionBisection", {
  lambda   <- c(3.0, 1.75)
  nu       <- rep(c(0.5, 1.5), 5L)
  sumZeros <- c(0.0, 5.0)

  d <- c(dispersionBisection(sumZeros = sumZeros[[1L]],
                             lambda = lambda[[1L]], nu = nu),
         dispersionBisection(sumZeros = sumZeros[[2L]],
                             lambda = lambda[[2L]], nu = nu))

  expect_identical(d, c(-Inf, 1.98046875))
})


test_that("nuBisection", {
  lambda       <- c(5.5,  4.0, 2.0, 1.0, 5.5, 1.5, 3.0, 3.5, 1.0, 4.5)
  dispersion   <- c(-Inf, 4.0, 2.5, 0.9, 5.0, 1.9, 3.5, 4.0, 0.9, 4.5)
  sumZeros     <- c(3L, 6L)
  initialGuess <- c(1.0, 1.5)

  nu <- c(nuBisection(sumZeros = sumZeros[[1L]], lambda = lambda,
                      dispersion = dispersion,
                      initialGuess = initialGuess[[1L]]),
          nuBisection(sumZeros = sumZeros[[2L]], lambda = lambda,
                      dispersion = dispersion,
                      initialGuess = initialGuess[[2L]]))

  expect_identical(nu, c(3.1484375, 0.349365234375))
})


test_that("plotTheme", {
  expect_warning(plotTheme(plotKind = "Wrong", textSize = 12.0))
})


test_that("Raw data normalization", {
  utils::data("test.dataset", package = "COTAN")
  genes.names.test <- readRDS(file.path(getwd(), "genes.names.test.RDS"))
  cells.names.test <- readRDS(file.path(getwd(), "cells.names.test.RDS"))

  raw <- test.dataset[genes.names.test, cells.names.test]

  nu <- readRDS(file.path(getwd(), "nu.test.RDS"))
  raw.norm <- as.matrix(readRDS(file.path(getwd(), "raw.norm.test.RDS")))

  expect_identical(t(t(raw) * (1.0 / nu)), raw.norm)
})


test_that("parallelDist - cosine dissimilarity", {
  raw <- matrix(c(1L,  0L, 4L, 2L, 11L, 0L, 6L, 7L, 0L, 9L,
                  10L, 8L, 0L, 0L,  0L, 3L, 0L, 0L, 2L, 0L),
                nrow = 10L, ncol = 20L)
  rownames(raw) <- LETTERS[1L:10L]
  colnames(raw) <- letters[1L:20L]

  cd <- as.matrix(parallelDist::parDist(t(raw), method = "cosine"))

  expect_equal(cd[(row(cd) + col(cd)) %% 2L == 1L], rep(cd[2L, 1L], 200L),
               tolerance = 1.0e-15)
  expect_equal(cd[(row(cd) + col(cd)) %% 2L == 0L], rep(cd[3L, 1L], 200L),
               tolerance = 1.0e-15)
})


test_that("pca usage", {
  utils::data("test.dataset", package = "COTAN")

  pcaRaw <- pca(mat = test.dataset, rank = 5L,
                transposed = TRUE, BSPARAM = IrlbaParam())[["rotated"]]
  colnames(pcaRaw) <- paste0("PC_", seq_len(ncol(pcaRaw)))

  expect_identical(rownames(pcaRaw), rownames(test.dataset))

  pcaExp <- readRDS(file.path(getwd(), "pca.test.RDS"))
  expect_identical(nrow(pcaRaw), nrow(pcaExp))

  pcaExp <- pcaExp[rownames(pcaRaw), ]

  correlations <- c(cor(pcaRaw[, 1L], pcaExp[, 1L]),
                    cor(pcaRaw[, 2L], pcaExp[, 2L]))

  dists <- sqrt(c(sum((pcaRaw[, 1L] - correlations[[1L]] * pcaExp[, 1L])^2L),
                  sum((pcaRaw[, 2L] - correlations[[2L]] * pcaExp[, 2L])^2L)))

  expect_lt(max(dists), 10.0^(-4L))
})


# legacy
test_that("vec2mat_rfast", {
  mat <- matrix(0.0, nrow = 10L, ncol = 10L)
  mat <- Rfast::lower_tri.assign(mat, (1L:55L), diag = TRUE)
  mat <- Rfast::upper_tri.assign(mat,
                                 v = Rfast::upper_tri(Rfast::transpose(mat)))

  colnames(mat) <- paste0("row.", (1L:10L))
  rownames(mat) <- paste0("row.", (1L:10L))

  genes <- paste0("row.", c(1L, 2L, 9L, 10L))

  expect_identical(mat, vec2mat_rfast(mat2vec_rfast(mat)))
  expect_identical(mat[, genes],
                   vec2mat_rfast(mat2vec_rfast(mat), genes = genes))
})


test_that("mat2vec_rfast", {
  names.v <- paste0("raw", (1L:15L))

  vec <- list("genes" = names.v, "values" = 1L:120L)

  expect_equal(vec, mat2vec_rfast(vec2mat_rfast(vec)), ignore_attr = TRUE)
})