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bbimber merged 3 commits into from
Aug 29, 2026
Merged

Update test tolerances #153

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a3cc26d
Update test tolerances
bbimber Aug 28, 2026
2a4c542
Update test tolerances
bbimber Aug 28, 2026
5c99634
Update test tolerances
bbimber Aug 29, 2026
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48 changes: 24 additions & 24 deletions tests/testthat/test-celltypist.R
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Original file line number Diff line number Diff line change
Expand Up @@ -47,16 +47,16 @@ test_that("celltypist runs", {

# This appears to differ in bioconductor 3.15 vs devel
# This should be identical to the test below
expect_equal(11, length(unique(seuratObj$majority_voting)), info = 'using default model', tolerance = 1)
expect_equal(11, length(unique(seuratObj$majority_voting)), info = 'using default model', tolerance = 1, scale = 1)
expect_equal(110, length(unique(seuratObj$predicted_labels)))
expect_equal(289, unname(table(seuratObj$predicted_labels)['B cells']))

# ensure the RIRA model works:
seuratObj <- RunCellTypist(seuratObj, modelName = 'RIRA_Immune_v2', columnPrefix = 'RIRA.')
print(sort(table(seuratObj$RIRA.majority_voting)))
expect_equal(4, length(unique(seuratObj$RIRA.majority_voting)), info = 'using RIRA model', tolerance = 1)
expect_equal(346, unname(table(seuratObj$RIRA.majority_voting)['Bcell']), tolerance = 1)
expect_equal(1653, unname(table(seuratObj$RIRA.majority_voting)['T_NK']), tolerance = 1)
expect_equal(4, length(unique(seuratObj$RIRA.majority_voting)), info = 'using RIRA model', tolerance = 1, scale = 1)
expect_equal(346, unname(table(seuratObj$RIRA.majority_voting)['Bcell']), tolerance = 1, scale = 1)
expect_equal(1653, unname(table(seuratObj$RIRA.majority_voting)['T_NK']), tolerance = 1, scale = 1)
expect_equal(686, unname(table(seuratObj$RIRA.majority_voting)['Myeloid']))

# NOTE: this is very slow, so skip in automated testing for now
Expand All @@ -74,9 +74,9 @@ test_that("celltypist runs", {

print(table(seuratObj$majority_voting))
print(table(seuratObj$predicted_labels))
expect_equal(10, length(unique(seuratObj$majority_voting)), info = 'using custom model', tolerance = 1)
expect_equal(54, length(unique(seuratObj$predicted_labels)), tolerance = 3)
expect_equal(356, unname(table(seuratObj$predicted_labels)['B cells']), tolerance = 2)
expect_equal(10, length(unique(seuratObj$majority_voting)), info = 'using custom model', tolerance = 1, scale = 1)
expect_equal(47, length(unique(seuratObj$predicted_labels)), tolerance = 3, scale = 1)
expect_equal(335, unname(table(seuratObj$predicted_labels)['B cells']), tolerance = 2, scale = 1)
})

test_that("celltypist runs with batchSize", {
Expand All @@ -93,8 +93,8 @@ test_that("celltypist runs with batchSize", {
print(table(seuratObj$majority_voting))

# This should be identical to the test above
expect_equal(13, length(unique(seuratObj$cellclass)), info = 'using default model', tolerance = 0)
expect_equal(28, length(unique(seuratObj$majority_voting)), info = 'using default model', tolerance = 1) # NOTE: getting different outcomes on devel vs. 3.16, perhaps due to some package difference? the difference is ambugious calls
expect_equal(13, length(unique(seuratObj$cellclass)), info = 'using default model')
expect_equal(28, length(unique(seuratObj$majority_voting)), info = 'using default model', tolerance = 1, scale = 1) # NOTE: getting different outcomes on devel vs. 3.16, perhaps due to some package difference? the difference is ambugious calls
expect_equal(110, length(unique(seuratObj$predicted_labels)))
expect_equal(289, unname(table(seuratObj$predicted_labels)['B cells']))
})
Expand All @@ -106,13 +106,13 @@ test_that("celltypist runs for RIRA models", {
print(table(seuratObj$RIRA_TNK_v2.cellclass))

expect_equal('RIRA_TNK_v2', seuratObj@misc$RIRA_TNK_Model)
expect_equal(4, length(unique(seuratObj$RIRA_TNK_v2.cellclass)), info = 'using RIRA T_NK', tolerance = 1)
expect_equal(221, unname(table(seuratObj$RIRA_TNK_v2.cellclass)['CD4+ T Cells']), tolerance = 1)
expect_equal(1028, unname(table(seuratObj$RIRA_TNK_v2.cellclass)['CD8+ T Cells']), tolerance = 1)
expect_equal(66, unname(table(seuratObj$RIRA_TNK_v2.cellclass)['NK Cells']), tolerance = 1)
expect_equal(1366, unname(table(seuratObj$RIRA_TNK_v2.cellclass)['Unassigned']), tolerance = 1)
expect_equal(4, length(unique(seuratObj$RIRA_TNK_v2.cellclass)), info = 'using RIRA T_NK', tolerance = 1, scale = 1)
expect_equal(221, unname(table(seuratObj$RIRA_TNK_v2.cellclass)['CD4+ T Cells']), tolerance = 1, scale = 1)
expect_equal(1028, unname(table(seuratObj$RIRA_TNK_v2.cellclass)['CD8+ T Cells']), tolerance = 1, scale = 1)
expect_equal(66, unname(table(seuratObj$RIRA_TNK_v2.cellclass)['NK Cells']), tolerance = 1, scale = 1)
expect_equal(1366, unname(table(seuratObj$RIRA_TNK_v2.cellclass)['Unassigned']), tolerance = 1, scale = 1)

expect_equal(6.64e-08, min(seuratObj$RIRA_TNK_v2.prob.NK.Cells), tolerance = 0.00001)
expect_equal(6.64e-08, min(seuratObj$RIRA_TNK_v2.prob.NK.Cells), tolerance = 0.00001, scale = 1)

seuratObj <- Classify_Myeloid(seuratObj, retainProbabilityMatrix = TRUE)
print('RIRA_Myeloid_v3:')
Expand All @@ -121,8 +121,8 @@ test_that("celltypist runs for RIRA models", {

expect_equal('RIRA_FineScope_Myeloid_v3', seuratObj@misc$RIRA_Myeloid_Model)
expect_equal(5, length(unique(seuratObj$RIRA_Myeloid_v3.cellclass)), info = 'using RIRA Myeloid')
expect_equal(32, unname(table(seuratObj$RIRA_Myeloid_v3.cellclass)['DC']), tolerance = 1)
expect_equal(32, unname(table(seuratObj$RIRA_Myeloid_v3.coarseclass)['DC']), tolerance = 1)
expect_equal(32, unname(table(seuratObj$RIRA_Myeloid_v3.cellclass)['DC']), tolerance = 1, scale = 1)
expect_equal(32, unname(table(seuratObj$RIRA_Myeloid_v3.coarseclass)['DC']), tolerance = 1, scale = 1)
})

test_that("FilterDisallowedClasses works as expected", {
Expand All @@ -136,18 +136,18 @@ test_that("FilterDisallowedClasses works as expected", {
print(table(seuratObj$RIRA_Immune_v2.cellclass))

expect_equal('RIRA_Immune_v2', seuratObj@misc$RIRA_Immune_Model)
expect_equal(256, sum(seuratObj$RIRA_Immune_v2.cellclass == 'Bcell', na.rm = T), tolerance = 1)
expect_equal(258, sum(seuratObj$RIRA_Immune_v2.cellclass == 'Bcell', na.rm = T), tolerance = 1, scale = 1)
expect_equal(577, sum(seuratObj$RIRA_Immune_v2.cellclass == 'Myeloid', na.rm = T))
expect_equal(1340, sum(seuratObj$RIRA_Immune_v2.cellclass == 'T_NK', na.rm = T))

print('DisallowedUCellCombinations:')
print(table(seuratObj$DisallowedUCellCombinations))

# NOTE: these are producing different results on 3.16 vs devel. This is possibly scGate versions?
expect_equal(347, sum(seuratObj$DisallowedUCellCombinations == 'NeutrophilLineage.RM_UCell', na.rm = T), tolerance = 3)
expect_equal(21, sum(seuratObj$DisallowedUCellCombinations == 'Erythrocyte.RM_UCell', na.rm = T), tolerance = 1)
expect_equal(55, sum(seuratObj$DisallowedUCellCombinations == 'NK.RM_UCell', na.rm = T), tolerance = 3)
expect_equal(57, sum(seuratObj$DisallowedUCellCombinations == 'Platelet.RM_UCell', na.rm = T), tolerance = 1)
expect_equal(301, sum(seuratObj$DisallowedUCellCombinations == 'NeutrophilLineage.RM_UCell', na.rm = T), tolerance = 3, scale = 1)
expect_equal(15, sum(seuratObj$DisallowedUCellCombinations == 'Erythrocyte.RM_UCell', na.rm = T), tolerance = 1, scale = 1)
expect_equal(51, sum(seuratObj$DisallowedUCellCombinations == 'NK.RM_UCell', na.rm = T), tolerance = 3, scale = 1)
expect_equal(51, sum(seuratObj$DisallowedUCellCombinations == 'Platelet.RM_UCell', na.rm = T), tolerance = 1, scale = 1)

# Create fake clustering:
print(table(seuratObj$RIRA_Immune_v2.cellclass, seuratObj$scGateConsensus))
Expand All @@ -160,7 +160,7 @@ test_that("FilterDisallowedClasses works as expected", {
expect_equal(1340, sum(seuratObj$RIRA_Immune_v2.cellclass == 'T_NK', na.rm = T))

expect_equal(336, sum(seuratObj$RIRA_Immune_v2.cellclass.recovered == 'Bcell', na.rm = T))
expect_equal(665, sum(seuratObj$RIRA_Immune_v2.cellclass.recovered == 'Myeloid', na.rm = T))
expect_equal(1615, sum(seuratObj$RIRA_Immune_v2.cellclass.recovered == 'T_NK', na.rm = T))
expect_equal(664, sum(seuratObj$RIRA_Immune_v2.cellclass.recovered == 'Myeloid', na.rm = T))
expect_equal(1616, sum(seuratObj$RIRA_Immune_v2.cellclass.recovered == 'T_NK', na.rm = T), tolerance = 1, scale = 1)

})
42 changes: 21 additions & 21 deletions tests/testthat/test-scgate.R
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Original file line number Diff line number Diff line change
Expand Up @@ -43,14 +43,14 @@ test_that("scGate Runs", {

# Try without reductions present:
seuratObj <- RunScGate(seuratObj, gate)
expect_equal(sum(seuratObj$is.pure == 'Pure'), 1486, info = 'Before DimRedux', tolerance = 1)
expect_equal(sum(seuratObj$is.pure == 'Pure'), 1500, info = 'Before DimRedux', tolerance = 1, scale = 1)

# Try with aliasing of models:
seuratObj <- RunScGateForModels(seuratObj, modelNames = c('Bcell', 'Tcell', 'NK', 'Myeloid'), labelRename = list(Tcell = 'T_NK', NK = 'T_NK'))
print(sort(table(seuratObj$scGateConsensus)))
dat <- table(seuratObj$scGateConsensus)
expect_equal(unname(dat[['Bcell']]), 244, info = 'With aliasing', tolerance = 2)
expect_equal(unname(dat[['T_NK']]), 1657, info = 'With aliasing', tolerance = 1)
expect_equal(unname(dat[['Bcell']]), 308, info = 'With aliasing', tolerance = 2, scale = 1)
expect_equal(unname(dat[['T_NK']]), 1645, info = 'With aliasing', tolerance = 1, scale = 1)

expect_false('Tcell' %in% names(dat), info = 'With aliasing')
expect_false('NK' %in% names(dat), info = 'With aliasing')
Expand All @@ -68,7 +68,7 @@ test_that("scGate Runs", {
if (packageVersion('UCell') < '2.5.0') {
expect_equal(sum(seuratObj$is.pure == 'Pure'), 1505, info = 'After DimRedux')
} else {
expect_equal(sum(seuratObj$is.pure == 'Pure'), 1493, info = 'After DimRedux', tolerance = 1)
expect_equal(sum(seuratObj$is.pure == 'Pure'), 1511, info = 'After DimRedux', tolerance = 1, scale = 1)
}

#At least execute this code once, so overt errors are caught
Expand All @@ -81,7 +81,7 @@ test_that("scGate works with built-in gates", {
# Use with built-in gate:
seuratObj <- getBaseSeuratData()
seuratObj <- RunScGate(seuratObj, model = 'Bcell')
expect_equal(sum(seuratObj$is.pure == 'Pure'), 340, tolerance = 1)
expect_equal(sum(seuratObj$is.pure == 'Pure'), 329, tolerance = 1, scale = 1)

})

Expand All @@ -95,7 +95,7 @@ test_that("scGates runs on all", {
print('RunScGateWithDefaultModels, using dropAmbiguousConsensusValues = FALSE')
print(dat)

expect_equal(unname(dat[['Bcell,Bcell.NonGerminalCenter,Immune,PanBcell']]), 285, tolerance = 1)
expect_equal(unname(dat[['Bcell,Bcell.NonGerminalCenter,Immune,PanBcell']]), 296, tolerance = 1, scale = 1)

# Now with ambiguous cleanup:
seuratObj <- RunScGateWithDefaultModels(seuratObj, dropAmbiguousConsensusValues = TRUE)
Expand All @@ -104,9 +104,9 @@ test_that("scGates runs on all", {
print(dat)
expect_false('MoMacDC,Myeloid' %in% names(dat))
if (packageVersion('UCell') < '2.5.0') {
expect_equal(unname(dat[['Immune']]), 7, tolerance = 1)
expect_equal(unname(dat[['Immune']]), 7, tolerance = 1, scale = 1)
} else {
expect_equal(unname(dat[['Immune']]), 131, tolerance = 1)
expect_equal(unname(dat[['Immune']]), 116, tolerance = 1, scale = 1)
}
})

Expand All @@ -127,15 +127,15 @@ test_that("scGate Runs", {
)
} else {
expected <- c(
Bcell.RM = 337,
Myeloid.RM = 676,
T_NK = 1647,
Bcell.RM = 333,
Myeloid.RM = 648,
T_NK = 1614,
'Bcell.RM,T_NK' = 15
)
}

for (pop in names(expected)) {
expect_equal(unname(dat[[pop]]), expected[[pop]], info = paste0('RM models: ', pop), tolerance = 3)
expect_equal(unname(dat[[pop]]), expected[[pop]], info = paste0('RM models: ', pop), tolerance = 3, scale = 1)
}

# Now use wrapper
Expand All @@ -145,28 +145,28 @@ test_that("scGate Runs", {
dat <- table(seuratObj$scGateConsensus)

expected <- c(
Bcell = 337,
Myeloid = 680,
T_NK = 1647,
Bcell = 332,
Myeloid = 650,
T_NK = 1614,
'Bcell,T_NK' = 15,
Platelet = 14
)

for (pop in names(expected)) {
expect_equal(unname(dat[[pop]]), expected[[pop]], info = 'RM models using wrapper', tolerance = 4)
expect_equal(unname(dat[[pop]]), expected[[pop]], info = 'RM models using wrapper', tolerance = 4, scale = 1)
}

print(sort(table(seuratObj$scGateRaw)))
dat <- table(seuratObj$scGateRaw)

expected <- c(
NK.RM = 72,
Myeloid.RM = 679,
Tcell.RM = 1301,
'NK.RM,Tcell.RM' = 274
NK.RM = 65,
Myeloid.RM = 648,
Tcell.RM = 1284,
'NK.RM,Tcell.RM' = 265
)

for (pop in names(expected)) {
expect_equal(unname(dat[[pop]]), expected[[pop]], info = 'RM models, raw calls', tolerance = 4)
expect_equal(unname(dat[[pop]]), expected[[pop]], info = 'RM models, raw calls', tolerance = 4, scale = 1)
}
})