tests/testthat/test-examples.R

In compbiomed/animalcules: Interactive microbiome analysis toolkit

data_dir <- system.file("extdata/MAE.rds", package = "animalcules")
MAE <- readRDS(data_dir)

# Summary Plot Top
test_that("filter_summary_bar_density() is working", {
  p <- filter_summary_bar_density(MAE,
    samples_discard = c("subject_2", "subject_4"),
    filter_type = "By Metadata",
    sample_condition = "AGE"
  )

  expect_equal(length(p), 7)
})

# Summary Plot Bottom
test_that("filter_summary_bar_density() is working", {
  p <- filter_summary_bar_density(MAE,
    samples_discard = c("subject_2", "subject_4"),
    filter_type = "By Metadata",
    sample_condition = "SEX"
  )
  expect_equal(length(p), 7)
})

# Categorize
test_that("filter_categorize() is working", {
  microbe <- MAE[["MicrobeGenetics"]]
  samples <- as.data.frame(colData(microbe))
  result <- filter_categorize(samples,
    sample_condition = "AGE",
    new_label = "AGE_GROUP",
    bin_breaks = c(0, 55, 75, 100),
    bin_labels = c("Young", "Adult", "Elderly")
  )
  expect_equal(dim(result$sam_table), c(50, 5))
  expect_equal(length(result$plot.unbinned), 7)
  expect_equal(length(result$plot.binned), 7)
})

## Relative Abundance Stacked Bar Plot
test_that("relabu_barplot() is working", {
  p <- relabu_barplot(MAE,
    tax_level = "family",
    order_organisms = c("Retroviridae"),
    sort_by = "organisms",
    sample_conditions = c("SEX", "AGE"),
    show_legend = TRUE
  )
  expect_equal(length(p), 7)
})

## Relative Abundance Heatmap
test_that("relabu_heatmap() is working", {
  p <- relabu_heatmap(MAE,
    tax_level = "genus",
    sort_by = "conditions",
    sample_conditions = c("SEX", "AGE")
  )
  expect_equal(length(p), 7)
})

## Relative Abundance Boxplot
test_that("relabu_boxplot() is working", {
  p <- relabu_boxplot(MAE,
    tax_level = "genus",
    organisms = c("Escherichia", "Actinomyces"),
    condition = "SEX",
    datatype = "logcpm"
  )
  expect_equal(length(p), 8)
})

## Alpha Diversity Boxplot
test_that("alpha_div_boxplot() is working", {
  p <- alpha_div_boxplot(
    MAE = MAE,
    tax_level = "genus",
    condition = "DISEASE",
    alpha_metric = "shannon"
  )
  expect_equal(length(p), 7)
})

## Alpha Diversity Statistical Test
test_that("do_alpha_div_test() is working", {
  p <- do_alpha_div_test(
    MAE = MAE,
    tax_level = "genus",
    condition = "DISEASE",
    alpha_metric = "shannon",
    alpha_stat = "T-test"
  )
  pval_wil <- round(p[1, 1, drop = TRUE], 2)
  pval_t <- round(p[1, 2, drop = TRUE], 2)

  expect_equal(pval_wil, 0.33)
  expect_equal(pval_t, 0.42)
})

## Beta Diversity Heatmap
test_that("diversity_beta_heatmap() is working", {
  p <- diversity_beta_heatmap(
    MAE = MAE,
    tax_level = "genus",
    input_beta_method = "bray",
    input_bdhm_select_conditions = "DISEASE",
    input_bdhm_sort_by = "condition"
  )
  expect_equal(length(p), 7)
})

## Beta Diversity Boxplot
test_that("diversity_beta_boxplot() is working", {
  p <- diversity_beta_boxplot(
    MAE = MAE,
    tax_level = "genus",
    input_beta_method = "bray",
    input_select_beta_condition = "DISEASE"
  )
  expect_equal(length(p), 7)
})

## Beta Diversity Test
test_that("diversity_beta_test() is working", {
  p <- diversity_beta_test(
    MAE = MAE,
    tax_level = "genus",
    input_beta_method = "bray",
    input_select_beta_condition = "DISEASE",
    input_select_beta_stat_method = "PERMANOVA",
    input_num_permutation_permanova = 999
  )
  expect_equal(p$Df[1], 1)
  expect_equal(p$Df[2], 48)
  expect_equal(p$Df[3], 49)
})

# PCA
test_that("dimred_pca() is working", {
  result <- dimred_pca(MAE,
    tax_level = "genus",
    color = "AGE",
    shape = "DISEASE",
    pcx = 1,
    pcy = 2,
    datatype = "logcpm"
  )
  expect_equal(dim(result$table), c(50, 4))
})

## PCoA
test_that("dimred_pcoa() is working", {
  result <- dimred_pcoa(MAE,
    tax_level = "genus",
    color = "AGE",
    shape = "DISEASE",
    axx = 1,
    axy = 2,
    method = "bray"
  )
  expect_equal(dim(result$table), c(50, 4))
})

## tSNE
test_that("dimred_tsne() is working", {
  result <- dimred_tsne(MAE,
    tax_level = "phylum",
    color = "AGE",
    shape = "GROUP",
    k = "3D",
    initial_dims = 30,
    perplexity = 10,
    datatype = "logcpm"
  )
  expect_equal(length(result$plot), 8)
})

# Differential Analysis
test_that("differential_abundance() is working", {
  p <- differential_abundance(MAE,
    tax_level = "phylum",
    input_da_condition = c("DISEASE"),
    min_num_filter = 2,
    input_da_padj_cutoff = 0.8
  )
  expect_equal(dim(p), c(8, 8))
})

# Biomarker
test_that("find_biomarker() is working", {
  p <- find_biomarker(MAE,
    tax_level = "genus",
    input_select_target_biomarker = c("SEX"),
    nfolds = 3,
    nrepeats = 3,
    seed = 99,
    percent_top_biomarker = 0.2,
    model_name = "logistic regression"
  )
  expect_equal(length(p), 3)
})

# inverse_simpson
test_that("inverse_simpson() is working", {
  p <- inverse_simpson(seq_len(10))
  p <- round(p, 2)
  expect_equal(p, 7.86)
})

# counts_to_relabu
test_that("counts_to_relabu() is working", {
  p <- counts_to_relabu(matrix(seq_len(12), 4))
  expect_equal(nrow(p), 4)
})

# counts_to_logcpm
test_that("counts_to_logcpm() is working", {
  p <- counts_to_logcpm(matrix(seq_len(12), 4))
  expect_equal(nrow(p), 4)
})

# mae_pick_samples
test_that("mae_pick_samples() is working", {
  p <- mae_pick_samples(MAE, isolate_samples = c("subject_9", "subject_14"))
  expect_equal(length(p), 2)
})

# df_char_to_factor
test_that("df_char_to_factor() is working", {
  p <- df_char_to_factor(matrix(seq_len(12)))
  expect_equal(nrow(p), 12)
})

# percent
test_that("percent() is working", {
  p <- percent(c(0.42, 0.15, 0.4, 0.563, 0.2))
  expect_equal(p[1], "42.00%")
})

# is_categorical
test_that("is_categorical() is working", {
  p <- is_categorical(2)
  expect_true(p)
})

# is_integer0
test_that("is_integer0() is working", {
  p <- is_integer0(2)
  expect_false(p)
})

# is_integer1
test_that("is_integer1() is working", {
  p <- is_integer1(2)
  expect_false(p)
})

# pct2str
test_that("pct2str() is working", {
  p <- pct2str(0.23)
  expect_equal(p, "23.00")
})

# shannon
test_that("shannon() is working", {
  p <- shannon(seq_len(10))
  p <- round(p, 2)
  expect_equal(p, 2.15)
})

# gini_simpson
test_that("gini_simpson() is working", {
  p <- gini_simpson(seq_len(10))
  p <- round(p, 2)
  expect_equal(p, 0.87)
})

# grep_tid
test_that("grep_tid() is working", {
  p <- grep_tid("ti|700015|org|Coriobacterium_glomerans_PW2")
  expect_equal(p, "700015")
})

# find_taxonomy
test_that("find_taxonomy() is working", {
  p <- find_taxonomy(1200)
  expect_equal(p$Taxon$Taxon$TaxId, "131567")
})

# diversities
test_that("diversities() is working", {
  p <- diversities(matrix(seq_len(12), nrow = 3), index = "shannon")
  expect_equal(round(p[1], 2), 1.01)
})

# diversities_help
test_that("diversities_help() is working", {
  p <- diversities_help(matrix(seq_len(12), nrow = 3), index = "shannon")
  expect_equal(round(p[1], 2), 1.01)
})

# alpha_div_test
test_that("alpha_div_test() is working", {
  df_test <- data.frame(
    richness = seq_len(10),
    condition = c(
      rep(1, 5),
      rep(0, 5)
    )
  )
  p <- alpha_div_test(df_test, alpha_stat = "Wilcoxon rank sum test")
  expect_equal(round(as.numeric(as.character(p$output[2])), 4), 0.0011)
})