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Merged
ncborcherding merged 3 commits into from
Feb 20, 2026
Merged

Expand unit tests v2 #4

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54981e9
Create test-utils-output.R
ncborcherding Feb 20, 2026
5bedc39
Create test-utils-validate.R
ncborcherding Feb 20, 2026
6d3f8a4
expand old tests
ncborcherding Feb 20, 2026
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172 changes: 160 additions & 12 deletions tests/testthat/test-clusterScoring.R
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Original file line number Diff line number Diff line change
Expand Up @@ -19,8 +19,6 @@ test_that("clusterScoring rejects non-data.frame cdr3_sequences", {
})

test_that("clusterScoring accepts character vector for cdr3_sequences", {
# Should convert to data.frame internally without error (unless cluster_list is empty)
# The function coerces atomic to data.frame, then proceeds
cl <- list(
"CRG-1" = data.frame(
CDR3b = c("CASSLAPGATNEKLFF", "CASSLDRGEVFF"),
Expand All @@ -35,13 +33,10 @@ test_that("clusterScoring accepts character vector for cdr3_sequences", {
stringsAsFactors = FALSE
)

# This should not error on the cdr3_sequences validation
# (may error later if immApex features are unavailable)
tryCatch(
clusterScoring(cluster_list = cl, cdr3_sequences = seqs,
refdb_beta = ref_df, sim_depth = 10, n_cores = 1),
error = function(e) {
# Accept errors that are NOT about cdr3_sequences format
expect_false(grepl("data.frame", e$message))
}
)
Expand Down Expand Up @@ -119,14 +114,12 @@ test_that("clusterScoring validates v_usage_freq format", {
cl <- list("CRG-1" = data.frame(CDR3b = "CASSLAPGATNEKLFF"))
seqs <- data.frame(CDR3b = "CASSLAPGATNEKLFF")

# Non-data.frame should error
expect_error(
clusterScoring(cluster_list = cl, cdr3_sequences = seqs,
v_usage_freq = c(0.1, 0.2)),
"v_usage_freq"
)

# Data frame with one column should error
expect_error(
clusterScoring(cluster_list = cl, cdr3_sequences = seqs,
v_usage_freq = data.frame(vgene = "TRBV5-1")),
Expand All @@ -138,20 +131,41 @@ test_that("clusterScoring validates cdr3_length_freq format", {
cl <- list("CRG-1" = data.frame(CDR3b = "CASSLAPGATNEKLFF"))
seqs <- data.frame(CDR3b = "CASSLAPGATNEKLFF")

# Non-data.frame should error
expect_error(
clusterScoring(cluster_list = cl, cdr3_sequences = seqs,
cdr3_length_freq = c(0.1, 0.2)),
"cdr3_length_freq"
)
})

test_that("clusterScoring returns empty data.frame for empty cluster_list", {
seqs <- data.frame(
CDR3b = c("CASSLAPGATNEKLFF", "CASSLDRGEVFF"),
TRBV = c("TRBV5-1", "TRBV6-2"),
stringsAsFactors = FALSE
)
ref_df <- data.frame(
CDR3b = c("CASSLAPGATNEKLFF", "CASSLDRGEVFF"),
TRBV = c("TRBV5-1", "TRBV6-2"),
stringsAsFactors = FALSE
)

result <- clusterScoring(
cluster_list = list(),
cdr3_sequences = seqs,
refdb_beta = ref_df,
sim_depth = 10,
n_cores = 1
)
expect_s3_class(result, "data.frame")
expect_equal(nrow(result), 0)
})

# ---- Computation tests -------------------------------------------------------

test_that("clusterScoring computes scores with custom refdb_beta data frame", {
skip_on_cran()

# Create a small reference
ref_df <- data.frame(
CDR3b = c("CASSLAPGATNEKLFF", "CASSLDRGEVFF", "CASSYLAGGRNTLYF",
"CASSLTGGEETQYF", "CASSLGGRETQYF", "CASSLGQAYEQYF",
Expand Down Expand Up @@ -191,8 +205,7 @@ test_that("clusterScoring computes scores with custom refdb_beta data frame", {
expect_true("network.size.score" %in% colnames(result))
expect_true("cdr3.length.score" %in% colnames(result))
expect_true("vgene.score" %in% colnames(result))
expect_equal(nrow(result), 1) # One cluster
# Scores should be numeric
expect_equal(nrow(result), 1)
expect_true(is.numeric(result$total.score))
})

Expand Down Expand Up @@ -233,9 +246,144 @@ test_that("clusterScoring gliph_version 2 omits 0.064 multiplier", {
refdb_beta = ref_df, gliph_version = 2, sim_depth = 10, n_cores = 1
)

# Both should return data frames with total.score
expect_s3_class(res_v1, "data.frame")
expect_s3_class(res_v2, "data.frame")
expect_true("total.score" %in% colnames(res_v1))
expect_true("total.score" %in% colnames(res_v2))
})

test_that("clusterScoring with counts and HLA information", {
skip_on_cran()

ref_df <- data.frame(
CDR3b = c("CASSLAPGATNEKLFF", "CASSLDRGEVFF", "CASSYLAGGRNTLYF",
"CASSLTGGEETQYF", "CASSLGGRETQYF", "CASSLGQAYEQYF"),
TRBV = c("TRBV5-1", "TRBV6-2", "TRBV5-1", "TRBV7-2", "TRBV5-1",
"TRBV6-2"),
stringsAsFactors = FALSE
)

cl <- list(
"CRG-1" = data.frame(
CDR3b = c("CASSLAPGATNEKLFF", "CASSLDRGEVFF", "CASSYLAGGRNTLYF"),
TRBV = c("TRBV5-1", "TRBV6-2", "TRBV5-1"),
patient = c("P1", "P2", "P1"),
HLA = c("A*02:01,B*07:02", "A*02:01,B*08:01", "A*02:01,B*07:02"),
counts = c(5, 3, 2),
stringsAsFactors = FALSE
)
)

seqs <- data.frame(
CDR3b = c("CASSLAPGATNEKLFF", "CASSLDRGEVFF", "CASSYLAGGRNTLYF",
"CASSLTGGEETQYF"),
TRBV = c("TRBV5-1", "TRBV6-2", "TRBV5-1", "TRBV7-2"),
patient = c("P1", "P2", "P1", "P2"),
HLA = c("A*02:01,B*07:02", "A*02:01,B*08:01",
"A*02:01,B*07:02", "A*02:01,B*08:01"),
counts = c(5, 3, 2, 1),
stringsAsFactors = FALSE
)

result <- clusterScoring(
cluster_list = cl,
cdr3_sequences = seqs,
refdb_beta = ref_df,
gliph_version = 1,
sim_depth = 10,
n_cores = 1
)

expect_s3_class(result, "data.frame")
expect_true("clonal.expansion.score" %in% colnames(result))
expect_true("hla.score" %in% colnames(result))
})

test_that("clusterScoring with multiple clusters", {
skip_on_cran()

ref_df <- data.frame(
CDR3b = c("CASSLAPGATNEKLFF", "CASSLDRGEVFF", "CASSYLAGGRNTLYF",
"CASSLTGGEETQYF", "CASSLGGRETQYF", "CASSLGQAYEQYF",
"CASSFSTCSANYGYTF", "CASSPTGGYEQYF"),
TRBV = c("TRBV5-1", "TRBV6-2", "TRBV5-1", "TRBV7-2", "TRBV5-1",
"TRBV6-2", "TRBV5-1", "TRBV7-2"),
stringsAsFactors = FALSE
)

cl <- list(
"CRG-1" = data.frame(
CDR3b = c("CASSLAPGATNEKLFF", "CASSLDRGEVFF"),
TRBV = c("TRBV5-1", "TRBV6-2"),
stringsAsFactors = FALSE
),
"CRG-2" = data.frame(
CDR3b = c("CASSYLAGGRNTLYF", "CASSLTGGEETQYF"),
TRBV = c("TRBV5-1", "TRBV7-2"),
stringsAsFactors = FALSE
)
)

seqs <- data.frame(
CDR3b = c("CASSLAPGATNEKLFF", "CASSLDRGEVFF", "CASSYLAGGRNTLYF",
"CASSLTGGEETQYF"),
TRBV = c("TRBV5-1", "TRBV6-2", "TRBV5-1", "TRBV7-2"),
stringsAsFactors = FALSE
)

result <- clusterScoring(
cluster_list = cl,
cdr3_sequences = seqs,
refdb_beta = ref_df,
gliph_version = 1,
sim_depth = 10,
n_cores = 1
)

expect_s3_class(result, "data.frame")
expect_equal(nrow(result), 2)
})

test_that("clusterScoring with custom v_usage_freq", {
skip_on_cran()

ref_df <- data.frame(
CDR3b = c("CASSLAPGATNEKLFF", "CASSLDRGEVFF", "CASSYLAGGRNTLYF",
"CASSLTGGEETQYF", "CASSLGGRETQYF"),
TRBV = c("TRBV5-1", "TRBV6-2", "TRBV5-1", "TRBV7-2", "TRBV5-1"),
stringsAsFactors = FALSE
)

cl <- list(
"CRG-1" = data.frame(
CDR3b = c("CASSLAPGATNEKLFF", "CASSLDRGEVFF"),
TRBV = c("TRBV5-1", "TRBV6-2"),
stringsAsFactors = FALSE
)
)

seqs <- data.frame(
CDR3b = c("CASSLAPGATNEKLFF", "CASSLDRGEVFF", "CASSYLAGGRNTLYF"),
TRBV = c("TRBV5-1", "TRBV6-2", "TRBV5-1"),
stringsAsFactors = FALSE
)

v_freq <- data.frame(
vgene = c("TRBV5-1", "TRBV6-2", "TRBV7-2"),
freq = c(0.5, 0.3, 0.2),
stringsAsFactors = FALSE
)

result <- clusterScoring(
cluster_list = cl,
cdr3_sequences = seqs,
refdb_beta = ref_df,
v_usage_freq = v_freq,
gliph_version = 1,
sim_depth = 10,
n_cores = 1
)

expect_s3_class(result, "data.frame")
expect_true("vgene.score" %in% colnames(result))
})
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