tests/testthat/test-filter-immundata-lev.R

In immundata: A Unified Data Layer for Large-Scale Single-Cell, Spatial and Bulk Immunomics

# 3. Levenshtein fuzzy matching
testthat::test_that("Levenshtein fuzzy matching returns correct results", {
  idata <- get_test_idata_tsv_no_metadata()
  all_receptors <- idata$receptors |> collect()

  pat <- substr(all_receptors$cdr3_aa[1:3], 1, nchar(all_receptors$cdr3_aa[1:3]) - 1)
  maxd <- 1
  out <- filter_immundata(
    idata,
    seq_options = make_seq_options(
      query_col = "cdr3_aa",
      patterns  = pat,
      method    = "lev",
      max_dist  = maxd,
      name_type = "pattern"
    )
  )
  dists <- adist(all_receptors$cdr3_aa, pat)
  gold <- all_receptors[apply(dists, 1, min) <= maxd, ]

  expect_equal(
    out$receptors |> collect() |> arrange(),
    gold |> arrange()
  )
})

# 6. Combined pre-filter and fuzzy matching
test_that("combined pre-filter and fuzzy matching works correctly", {
  idata <- get_test_idata_tsv_no_metadata()
  all_receptors <- idata$receptors |> collect()

  vc <- all_receptors$v_call[5]
  pat <- substr(all_receptors$cdr3_aa[5], 1, nchar(all_receptors$cdr3_aa[5]) - 1)
  maxd <- 1
  out <- filter_immundata(
    idata,
    v_call == vc,
    seq_options = make_seq_options(
      query_col = "cdr3_aa",
      patterns  = pat,
      method    = "lev",
      max_dist  = maxd,
      name_type = "pattern"
    )
  )
  sub <- all_receptors |> filter(v_call == vc)
  dists <- adist(sub$cdr3_aa, pat)
  gold <- sub[dists <= maxd, ]
  expect_equal(
    out$receptors |> collect() |> arrange(cdr3_aa),
    gold |> arrange(cdr3_aa)
  )
})