tests/testthat/test_distance_funcs.R

In PerkinElmer/phenoptr: inForm Helper Functions

# Tests for distance functions
# Mostly smoke tests to make sure the code runs...
library(testthat)

path = file.path('test_data',
              'FIHC4__0929309_HP_IM3_2_cell_seg_data.txt')

# Compute the Euclidean distance between two cells given their row numbers
cell_dist = function(csd, ix1, ix2) {
  d = csd[c(ix1, ix2), c('Cell X Position', 'Cell Y Position')]
  purrr::map_dbl(d, ~diff(.)^2) %>% sum %>% sqrt
}

test_that("distance_matrix works", {
  csd = read_cell_seg_data(path)
  dst = distance_matrix(csd)
  expect_equal(dim(dst), c(nrow(csd), nrow(csd)))

  # Spot check a few values by hand-calculation
  pairs = list(c(1, 2), c(10, 90), c(200, 250))
  for (pair in pairs) {
    ix1 = pair[1]
    ix2 = pair[2]
    info = paste(pair, collapse=', ')
    expect_equal(dst[ix1, ix2], dst[ix2, ix1], info=info) # Should be symmetric
    expect_equal(dst[ix1, ix2], cell_dist(csd, ix1, ix2), info=info)
  }

  s = subset_distance_matrix(csd, dst, 'B', 'Helper T')
  expect_equal(sum(csd$Phenotype=='B'), 249) # Make sure this is a real test
  expect_equal(sum(csd$Phenotype=='Helper T'), 15)
  expect_equal(dim(s)[1], sum(csd$Phenotype=='B'))
  expect_equal(dim(s)[2], sum(csd$Phenotype=='Helper T'))
  expect_error(subset_distance_matrix(dst, csd, 'B', 'Helper T'), 'wrong order')
})

test_that("find_nearest_distance works", {
  csd = read_cell_seg_data(path)
  phenos = sort(unique(csd$Phenotype))
  nearest = find_nearest_distance(csd)
  expect_equal(ncol(nearest), length(phenos))
  expect_equal(nrow(nearest), nrow(csd))
  expect_equal(names(nearest), paste('Distance to', phenos))

  # Spot check some cells where nearest neighbors are clear from inspection
  dst = distance_matrix(csd)
  pairs = list(
    c(64, 118),
    c(16, 30),
    c(187, 214),
    c(209, 215)
  )
  for (pair in pairs) {
    ix1 = pair[1]
    ix2 = pair[2]
    info = paste(ix1, ix2, sep=', ')
    pheno1 = paste('Distance to', csd$Phenotype[ix1])
    pheno2 = paste('Distance to', csd$Phenotype[ix2])
    dist = as.numeric(dst[ix1, ix2])
    expect_equal(as.numeric(nearest[ix1, pheno2]), dist, info=info)
    expect_equal(as.numeric(nearest[ix2, pheno1]), dist, info=info)
  }

  phenos = c(phenos, 'other') # A mising phenotype
  nearest = find_nearest_distance(csd, phenos)
  expect_equal(ncol(nearest), length(phenos))
  expect_equal(nearest$`Distance to other`, rep(NA_real_, nrow(csd)))

  # Force an error
  csd$`Sample Name`[1] = 'foo.im3'
  expect_error(find_nearest_distance(csd), 'multiple')
})

test_that('compute_all_nearest_distance works', {
  merge_path = file.path('test_data',
              'merge/FIHC4_merge_cell_seg_data.txt')
  out_path = tempfile()
  compute_all_nearest_distance(merge_path, out_path)
  expect_true(file.exists(out_path))
  all_d = readr::read_tsv(out_path, na=c('NA', '#N/A'), col_types=readr::cols())
  expect_equal(nrow(all_d), 66+68+270+215)

  # The result for the sample data should be the same
  csd = read_cell_seg_data(path)
  nearby = find_nearest_distance(csd)
  all_d_2 = all_d %>%
    dplyr::filter(`Sample Name`=="FIHC4__0929309_HP_IM3_2.im3") %>%
    dplyr::arrange(`Cell ID`) %>%
    dplyr::select(`Distance to B`,
                  `Distance to Cytotoxic T`,
                  `Distance to Helper T`)
  expect_equal(all_d_2, nearby)

  file.remove(out_path)
})