tests/testthat/test_transformation.R
In Genentech/MiDAS: R package for immunogenomics data handling and association analysis
context("Transforming MiDAS objects")
test_that("hlaToAAVariation", {
aa_variation <-
hlaToAAVariation(MiDAS_tut_HLA,
indels = TRUE,
unkchar = TRUE,
as_df = FALSE)
load(system.file("extdata", "test_aa_variation.Rdata", package = "midasHLA"))
expect_equal(aa_variation, test_aa_variation)
expect_error(hlaToAAVariation(MiDAS_tut_HLA, indels = "foo"),
"indels is not a flag \\(a length one logical vector\\)."
)
expect_error(hlaToAAVariation(MiDAS_tut_HLA, unkchar = "foo"),
"unkchar is not a flag \\(a length one logical vector\\)."
)
expect_error(hlaToAAVariation(MiDAS_tut_HLA, as_df = 1),
"as_df is not a flag \\(a length one logical vector\\)."
)
})
test_that("hlaToVariable", {
hla_supertypes <- hlaToVariable(MiDAS_tut_HLA, dictionary = "allele_HLA_supertype")
test_hla_supertypes <-
lapply(
MiDAS_tut_HLA[,-1],
convertAlleleToVariable,
dictionary = system.file("extdata", "Match_allele_HLA_supertype.txt", package = "midasHLA")
)
na_mask <- vapply(test_hla_supertypes, function(x) all(is.na(x)), FUN.VALUE = logical(1))
test_hla_supertypes <- test_hla_supertypes[! na_mask]
test_hla_supertypes <- do.call(cbind, test_hla_supertypes)
colnames(test_hla_supertypes) <-
paste0("supertype_", colnames(test_hla_supertypes))
test_hla_supertypes <-
cbind(MiDAS_tut_HLA[, 1, drop = FALSE], test_hla_supertypes, stringsAsFactors = FALSE)
expect_equal(hla_supertypes, test_hla_supertypes)
expect_error(
hlaToVariable(c("A*01:01", "A*02:01"), dictionary = "4digit_supertype"),
"hla_calls is not a data frame"
)
expect_error(
hlaToVariable(
hla_calls = MiDAS_tut_HLA,
dictionary = "4digit_supertype",
reduce = "yes"
),
"reduce is not a flag \\(a length one logical vector\\)."
)
expect_error(
hlaToVariable(
hla_calls = MiDAS_tut_HLA,
dictionary = "4digit_supertype",
na.value = 1:5
),
"na.value length must equal 1."
)
expect_error(
hlaToVariable(
hla_calls = MiDAS_tut_HLA,
dictionary = "4digit_supertype",
nacols.rm = "yes"
),
"nacols.rm is not a flag \\(a length one logical vector\\)."
)
})
test_that("hlaCallsToCounts", {
hla_calls <- data.frame(
ID = c("PAT1", "PAT2", "PAT3", "PAT4"),
A_1 = c("A*01", "A*02", NA, "A*01"),
A_2 = c("A*02", "A*01", "A*02", "A*01"),
stringsAsFactors = FALSE
)
hla_counts <- hlaCallsToCounts(hla_calls)
test_hla_counts <- data.frame(
ID = c("PAT1", "PAT2", "PAT3", "PAT4"),
"A*01" = c(1, 1, 0, 2),
"A*02" = c(1, 1, 1, 0),
check.names = FALSE,
stringsAsFactors = FALSE
)
expect_equal(hla_counts, test_hla_counts)
# check if non HLA data frame can be properly processed
nonhla <-
data.frame(
ID = 1:2,
A_1 = c("a1", "a2"),
A_2 = c("a2", "a2"),
stringsAsFactors = FALSE
)
nonhla_counts <- hlaCallsToCounts(nonhla, check_hla_format = FALSE)
expect_equal(nonhla_counts,
data.frame(
ID = 1:2,
a1 = c(1, 0),
a2 = c(1, 2),
stringsAsFactors = FALSE
))
expect_error(
hlaCallsToCounts(c("A*01:01", "A*02:01")),
"hla_calls is not a data frame"
)
expect_error(
hlaCallsToCounts(
hla_calls,
check_hla_format = 1
),
"check_hla_format is not a flag \\(a length one logical vector\\)."
)
})
test_that("getHlaFrequencies", {
minimal_hla_calls <- data.frame(
ID = c("P1", "P2"),
A_1 = c("A*01:01", "A*02:01"),
A_2 = c("A*02:01", "A*01:01"),
stringsAsFactors = FALSE
)
hla_freq <- getHlaFrequencies(minimal_hla_calls)
test_hla_freq <- data.frame(
allele = c("A*01:01", "A*02:01"),
Counts = c(2, 2),
Freq = c(0.5, 0.5),
row.names = NULL,
stringsAsFactors = FALSE
)
expect_equal(hla_freq, test_hla_freq)
})
test_that("getKIRFrequencies", {
kir_freq <- getKIRFrequencies(MiDAS_tut_KIR)
test_kir_freq <- data.frame(
gene = c("KIR3DL3", "KIR2DS2", "KIR2DL2", "KIR2DL3", "KIR2DP1", "KIR2DL1", "KIR3DP1", "KIR2DL4",
"KIR3DL1", "KIR3DS1", "KIR2DL5", "KIR2DS3", "KIR2DS5", "KIR2DS4", "KIR2DS1", "KIR3DL2"),
Counts = c(935, 455, 449, 849, 925, 918, 935, 935, 853, 365, 485, 294, 288, 853, 371, 935),
Freq = c(1, 0.486631016042781, 0.480213903743316, 0.908021390374332, 0.989304812834225, 0.981818181818182,
1, 1, 0.912299465240642, 0.390374331550802, 0.518716577540107, 0.314438502673797,
0.308021390374332, 0.912299465240642, 0.396791443850267, 1),
row.names = NULL,
stringsAsFactors = FALSE
)
expect_equal(kir_freq, test_kir_freq)
})
test_that("hlaToAAVariation", {
minimal_hla_calls <- data.frame(
ID = c("P1", "P2"),
A_1 = c("A*01:01", "A*02:01"),
A_2 = c("A*02:01", "A*01:01"),
stringsAsFactors = FALSE
)
aa_var <- hlaToAAVariation(minimal_hla_calls)[, 1:5]
aa_counts <- aaVariationToCounts(aa_var)
test_aa_counts <- data.frame(
ID = c("P1", "P2"),
A_44_K = c(1, 1),
A_44_R = c(1, 1),
A_62_Q = c(1, 1),
A_62_G = c(1, 1),
stringsAsFactors = FALSE, check.names = FALSE
)
expect_equal(aa_counts, test_aa_counts)
expect_error(
aaVariationToCounts(c("x", "c")),
"aa_variation is not a data frame"
)
expect_error(
aaVariationToCounts(aa_var[, -1]),
"first column of aa_variation must be named ID"
)
})
test_that("getAAFrequencies", {
minimal_hla_calls <- data.frame(
ID = c("P1", "P2"),
A_1 = c("A*01:01", "A*02:01"),
A_2 = c("A*02:01", "A*01:01"),
stringsAsFactors = FALSE
)
aa_var <- hlaToAAVariation(minimal_hla_calls)[, 1:5]
aa_freq <- getAAFrequencies(aa_var)
test_aa_freq <- data.frame(
aa_pos = c("A_44_K", "A_44_R", "A_62_Q", "A_62_G"),
Counts = c(2, 2, 2, 2),
Freq = c(0.5, 0.5, 0.5, 0.5),
row.names = NULL,
stringsAsFactors = FALSE
)
expect_equal(aa_freq, test_aa_freq)
expect_error(
aaVariationToCounts(c("x", "c")),
"aa_variation is not a data frame"
)
expect_error(
aaVariationToCounts(aa_var[, -1]),
"first column of aa_variation must be named ID"
)
})
test_that("formatResults", {
res <- tibble(term = c("A*01:01", "A*02:01"),
estimate = c(5, -6),
p.value = c(0.05, 0.1))
restab <- formatResults(res,
filter_by = c("p.value <= 0.05", "estimate > 0"),
arrange_by = c("p.value * estimate"),
select_cols = c("allele" = "term", "p.value"),
format = "html",
header = "informative header")
correct_tab <-
dplyr::filter(res, .data[["p.value"]] <= 0.05, .data[["estimate"]] > 0)
correct_tab <-
dplyr::arrange(correct_tab, .data[["p.value"]] * .data[["estimate"]])
correct_tab <-
dplyr::select(correct_tab, "allele" = .data[["term"]], .data[["p.value"]])
correct_tab <-
knitr::kable(correct_tab, format = "html", format.args = list(digits = 4, scientific = -3))
correct_tab <-
kableExtra::add_header_above(correct_tab, header = c("informative header" = 2))
correct_tab <-
kableExtra::kable_styling(correct_tab,
bootstrap_options = c("striped", "hover", "condensed"))
correct_tab <-
kableExtra::scroll_box(correct_tab, width = "100%", height = "400px")
expect_equal(restab, correct_tab)
expect_error(formatResults(res, filter_by = 1),
"filter_by is not a character vector")
expect_error(formatResults(res, arrange_by = 1),
"arrange_by is not a character vector")
expect_error(formatResults(res, select_cols = 1),
"select_cols is not a character vector")
expect_error(formatResults(res, format = 1),
"format is not a string \\(a length one character vector\\).")
expect_error(formatResults(res, format = "markdown"),
"format should be one of \"html\", \"latex\".")
expect_error(formatResults(res, format ="html", header = 1),
"header is not a string \\(a length one character vector\\) or NULL.")
expect_error(formatResults(res, format ="html", scroll_box_height = 1),
"scroll_box_height is not a string \\(a length one character vector\\).")
})
test_that("kableResults", {
midas <- prepareMiDAS(hla_calls = MiDAS_tut_HLA,
colData = MiDAS_tut_pheno,
experiment = "hla_alleles")
object <- lm(disease ~ term, data = midas)
res <- runMiDAS(object, inheritance_model = "additive", experiment = "hla_alleles")
res_kable <- kableResults(res)
res_kable_test <- formatResults(res,
filter_by = "p.value <= 1",
arrange_by = "p.value",
select_cols = c("allele",
"p.value",
"p.adjusted",
"estimate",
"std.error",
"conf.low",
"conf.high",
"statistic",
"Ntotal",
"Ntotal [%]" = "Ntotal.percent",
"N(disease=0)",
"N(disease=0) [%]" = "N(disease=0).percent",
"N(disease=1)",
"N(disease=1) [%]" = "N(disease=1).percent"
),
format = "html",
header = "MiDAS analysis results"
)
expect_equal(res_kable, res_kable_test)
expect_error(kableResults(LETTERS), "results is not a data frame")
expect_error(kableResults(res, 1:5), "colnames is not a character vector or NULL.")
expect_error(kableResults(res, pvalue_cutoff = "a"),
"pvalue_cutoff is not a number \\(a length one numeric vector\\) or NULL."
)
expect_error(kableResults(res, format = 1),
"format is not a string \\(a length one character vector\\)."
)
expect_error(kableResults(res, format = "foo"),
"format should be one of \"html\", \"latex\"."
)
expect_error(
kableResults(res, c("foo", "bar")),
"colnames should match values \"allele\", \"p.value\", \"p.adjusted\", \"estimate\", \"std.error\", \"conf.low\", \"conf.high\", \"statistic\", \"Ntotal\", \"Ntotal.percent\", \"N\\(disease=0\\)\", \"N\\(disease=0\\).percent\", \"N\\(disease=1\\)\", \"N\\(disease=1\\).percent\"."
)
expect_error(kableResults(res, scroll_box_height = 1),
"scroll_box_height is not a string \\(a length one character vector\\)."
)
})
test_that("countsToVariables", {
kir_haplotypes <- countsToVariables(MiDAS_tut_KIR[1:2, ], "kir_haplotypes")
kir_haplotypes_test <- data.frame(
ID = c("C001", "C002"),
cenAA = c(1, 1),
cenBB = c(0, 0),
cenAB = c(0, 0),
telAA = c(1, 1),
telBB = c(0, 0),
telAB = c(0, 0),
stringsAsFactors = FALSE
)
expect_equal(kir_haplotypes, kir_haplotypes_test)
# works with a dictionary data frame
dictionary <- data.frame(
Name = c("cenAA", "cenAB", "telAA"),
Expression = c(
"KIR2DL3 & ! KIR2DL2 & ! KIR2DS2",
"! (KIR2DL2 & ! KIR2DL3) & ! (KIR2DL3 & ! KIR2DL2 & ! KIR2DS2)",
"! KIR2DS1 & ! KIR3DS1"
),
stringsAsFactors = FALSE
)
kir_haplotypes <- countsToVariables(MiDAS_tut_KIR[1:2, ], dictionary)
expect_equal(kir_haplotypes,
kir_haplotypes_test[, c("ID", "cenAA", "cenAB", "telAA")]
)
expect_error(
countsToVariables(iris),
"first column in counts must be named 'ID'"
)
expect_error(
countsToVariables(MiDAS_tut_KIR, na.value = 1:2),
"na.value length must equal 1."
)
expect_error(
countsToVariables(MiDAS_tut_KIR, nacols.rm = 1),
"nacols.rm is not a flag \\(a length one logical vector\\)."
)
expect_error(
countsToVariables(MiDAS_tut_KIR, dictionary = "foo"),
"Path 'foo' does not exist"
)
expect_error(
countsToVariables(MiDAS_tut_KIR, dictionary = c("foo", "bar")),
"dictionary is not a data frame"
)
})
test_that("getHlaKirInteractions", {
hla_kir <- getHlaKirInteractions(MiDAS_tut_HLA, MiDAS_tut_KIR)
load(system.file("extdata", "test_hla_kir_interactions.Rdata", package = "midasHLA"))
expect_equal(hla_kir, test_hla_kir_interactions)
expect_error(
getHlaKirInteractions(MiDAS_tut_HLA, MiDAS_tut_KIR, interactions_dict = 1),
"interactions_dict is not a string \\(a length one character vector\\)."
)
fake_kir_counts <- MiDAS_tut_KIR
fake_kir_counts[, 1] <- paste0("foo", 1:nrow(fake_kir_counts))
expect_error(
getHlaKirInteractions(MiDAS_tut_HLA, fake_kir_counts),
"IDs in hla_calls doesn't match IDs in kir_calls"
)
fake_kir_counts <- MiDAS_tut_KIR
fake_kir_counts[1:5, 1] <- paste0("foo", 1:5)
expect_warning(
getHlaKirInteractions(MiDAS_tut_HLA, fake_kir_counts),
"995 IDs in hla_calls matched IDs in kir_calls"
)
})
test_that("filterExperimentByFrequency", {
# filtering works as expected for fractions
experiment <- MiDAS_tut_object[["hla_alleles"]]
lower_frequency_cutoff <- 0.50
upper_frequency_cutoff <- 0.90
experiment_filtered <- filterExperimentByFrequency(
experiment = experiment,
lower_frequency_cutoff = lower_frequency_cutoff,
upper_frequency_cutoff = upper_frequency_cutoff
)
expected_vars <- c("DPA1*01:03", "DRA*01:01")
expect_equal(experiment_filtered, experiment[expected_vars, ])
# filtering works as expected for counts
experiment <- MiDAS_tut_object[["hla_alleles"]]
lower_frequency_cutoff <- 8
upper_frequency_cutoff <- 10
experiment_filtered <- filterExperimentByFrequency(
experiment = experiment,
lower_frequency_cutoff = lower_frequency_cutoff,
upper_frequency_cutoff = upper_frequency_cutoff
)
# order of rows somehow depends on the R version...
expected_vars <-
c("B*27:02",
"B*41:01",
"B*41:02",
"C*08:03",
"DPA1*02:06",
"DRB1*01:03",
"DRB1*08:04")
expected_vars <- expected_vars[order(expected_vars)]
experiment_filtered <-
experiment_filtered[order(rownames(experiment_filtered)),]
expect_equal(experiment_filtered, experiment[expected_vars, ])
# filtering works as expected for boundry conditions NULL, NULL
experiment <- MiDAS_tut_object[["hla_alleles"]]
lower_frequency_cutoff <- NULL
upper_frequency_cutoff <- NULL
experiment_filtered <- filterExperimentByFrequency(
experiment = experiment,
lower_frequency_cutoff = lower_frequency_cutoff,
upper_frequency_cutoff = upper_frequency_cutoff
)
expect_equal(experiment_filtered, experiment)
# filtering works as expected for boundry conditions 0, 0
experiment <- MiDAS_tut_object[["hla_alleles"]]
lower_frequency_cutoff <- 0
upper_frequency_cutoff <- 0
experiment_filtered <- filterExperimentByFrequency(
experiment = experiment,
lower_frequency_cutoff = lower_frequency_cutoff,
upper_frequency_cutoff = upper_frequency_cutoff
)
expected_vars <- character(0L)
expect_equal(experiment_filtered, experiment[expected_vars, ])
# filtering works as expected for boundry conditions 1, 1
experiment <- MiDAS_tut_object[["hla_alleles"]]
lower_frequency_cutoff <- 1
upper_frequency_cutoff <- 1
experiment_filtered <- filterExperimentByFrequency(
experiment = experiment,
lower_frequency_cutoff = lower_frequency_cutoff,
upper_frequency_cutoff = upper_frequency_cutoff
)
expected_vars <- character(0L)
expect_equal(experiment_filtered, experiment[expected_vars, ])
# omnibus groups are filtred correctly
experiment <- MiDAS_tut_object[["hla_aa"]]
lower_frequency_cutoff <- 0.86
upper_frequency_cutoff <- 0.87
experiment_filtered <- filterExperimentByFrequency(
experiment = experiment,
lower_frequency_cutoff = lower_frequency_cutoff,
upper_frequency_cutoff = upper_frequency_cutoff
)
test_experiment <- experiment[c("C_35_R", "C_309_V"), ]
expect_equal(experiment_filtered, test_experiment)
# experiment must be of type integer
experiment <- matrix(LETTERS)
lower_frequency_cutoff <- 0.1
upper_frequency_cutoff <- 0.5
expect_error(
filterExperimentByFrequency(
experiment = experiment,
lower_frequency_cutoff = lower_frequency_cutoff,
upper_frequency_cutoff = upper_frequency_cutoff
),
"Frequency filtration does not support provided experiment."
)
# carrier_frequency must be a string
experiment <- MiDAS_tut_object[["hla_alleles"]]
inheritance_model <- 1
lower_frequency_cutoff <- "foo"
upper_frequency_cutoff <- 0.5
expect_error(
filterExperimentByFrequency(
experiment = experiment,
carrier_frequency = 1,
lower_frequency_cutoff = lower_frequency_cutoff,
upper_frequency_cutoff = upper_frequency_cutoff
),
"carrier_frequency is not a flag \\(a length one logical vector\\)."
)
# lower_frequency_cutof must be a number
experiment <- MiDAS_tut_object[["hla_alleles"]]
lower_frequency_cutoff <- "foo"
upper_frequency_cutoff <- 0.5
expect_error(
filterExperimentByFrequency(
experiment = experiment,
lower_frequency_cutoff = lower_frequency_cutoff,
upper_frequency_cutoff = upper_frequency_cutoff
),
"lower_frequency_cutoff is not a number \\(a length one numeric vector\\)."
)
# lower_frequency_cutof must be positive
experiment <- MiDAS_tut_object[["hla_alleles"]]
lower_frequency_cutoff <- -1
upper_frequency_cutoff <- 0.5
expect_error(
filterExperimentByFrequency(
experiment = experiment,
lower_frequency_cutoff = lower_frequency_cutoff,
upper_frequency_cutoff = upper_frequency_cutoff
),
"lower_frequency_cutoff must be a number greater than 0."
)
# upper_frequency_cutoff must be a number
experiment <- MiDAS_tut_object[["hla_alleles"]]
lower_frequency_cutoff <- 0.5
upper_frequency_cutoff <- "foo"
expect_error(
filterExperimentByFrequency(
experiment = experiment,
lower_frequency_cutoff = lower_frequency_cutoff,
upper_frequency_cutoff = upper_frequency_cutoff
),
"upper_frequency_cutoff is not a number \\(a length one numeric vector\\)."
)
# upper_frequency_cutoff must be positive
experiment <- MiDAS_tut_object[["hla_alleles"]]
lower_frequency_cutoff <- 0
upper_frequency_cutoff <- -1
expect_error(
filterExperimentByFrequency(
experiment = experiment,
lower_frequency_cutoff = lower_frequency_cutoff,
upper_frequency_cutoff = upper_frequency_cutoff
),
"upper_frequency_cutoff must be a number greater than 0."
)
# lower_frequency_cutoff is lower than upper_frequency_cutoff
experiment <- MiDAS_tut_object[["hla_alleles"]]
lower_frequency_cutoff <- 5
upper_frequency_cutoff <- 1
expect_error(
filterExperimentByFrequency(
experiment = experiment,
lower_frequency_cutoff = lower_frequency_cutoff,
upper_frequency_cutoff = upper_frequency_cutoff
),
"lower_frequency_cutoff cannot be higher than upper_frequency_cutoff."
)
# Both lower_frequency_cutoff and upper_frequency_cutoff have to be either frequencies or counts
experiment <- MiDAS_tut_object[["hla_alleles"]]
lower_frequency_cutoff <- 0.5
upper_frequency_cutoff <- 2
expect_error(
filterExperimentByFrequency(
experiment = experiment,
lower_frequency_cutoff = lower_frequency_cutoff,
upper_frequency_cutoff = upper_frequency_cutoff
),
"Both lower_frequency_cutoff and upper_frequency_cutoff have to be either frequencies or counts."
)
})
test_that("getExperimentFrequencies", {
experiment_matrix <- matrix(
data = c(0, 2, 0, 0, 0,
0, 2, 0, 0, 0,
2, 0, 0, 0, 0,
0, 1, 1, 0, 0,
0, 0, 0, 0, 0
),
nrow = 5,
ncol = 5,
dimnames = list(
c("A*01:01", "A*02:01", "A*02:06", "A*03:01", "A*23:01"),
c("PAT1", "PAT2", "PAT3", "PAT4", "PAT5")
)
)
experiment_se <- SummarizedExperiment::SummarizedExperiment(
assays = list(experiment_matrix),
metadata = list(
inheritance_model_applicable = TRUE,
pop_mul = 2,
omnibus_groups = NULL
)
)
# allele frequecny
experiment_freq_test <- data.frame(
term = c("A*01:01", "A*02:01", "A*02:06", "A*03:01", "A*23:01"),
Counts = c(2, 5, 1, 0, 0),
Freq = c(0.2, 0.5, 0.1, 0, 0),
row.names = NULL,
stringsAsFactors = FALSE
)
# matrix
expect_equal(getExperimentFrequencies(experiment_matrix, 2), experiment_freq_test)
# se
experiment_freq <-
getExperimentFrequencies(experiment_se)
expect_equal(experiment_freq, experiment_freq_test)
# carrier frequency
experiment_freq <-
getExperimentFrequencies(experiment_se, carrier_frequency = TRUE)
experiment_freq_test <- data.frame(
term = c("A*01:01", "A*02:01", "A*02:06", "A*03:01", "A*23:01"),
Counts = c(1, 3, 1, 0, 0),
Freq = c(0.2, 0.6, 0.2, 0, 0),
row.names = NULL,
stringsAsFactors = FALSE
)
expect_equal(experiment_freq, experiment_freq_test)
expect_error(
getExperimentFrequencies(matrix(LETTERS)),
"values in experiment are not counts \\(a positive integers\\) or zeros."
)
expect_error(getExperimentFrequencies(experiment_matrix),
"pop_mul is not a number \\(a length one numeric vector\\)."
)
expect_error(getExperimentFrequencies(experiment_matrix, 1, carrier_frequency = 1),
"carrier_frequency is not a flag \\(a length one logical vector\\).")
expect_error(getExperimentFrequencies(experiment_matrix, 1, ref = "foo"),
"ref is not a data frame")
})
test_that("applyInheritanceModel", {
experiment <- matrix(c(2, 0, 1, 1, 2, 1, 0, 2, 2), nrow = 3)
inheritance_model <- "additive"
additive <- applyInheritanceModel(experiment, inheritance_model)
expect_equal(experiment, additive)
inheritance_model <- "dominant"
dominant <- applyInheritanceModel(experiment, inheritance_model)
test_dominant <- matrix(c(1, 0, 1, 1, 1, 1, 0, 1, 1), nrow = 3)
expect_equal(dominant, test_dominant)
inheritance_model <- "recessive"
recessive <- applyInheritanceModel(experiment, inheritance_model)
test_recessive <- matrix(c(1, 0, 0, 0, 1, 0, 0, 1, 1), nrow = 3)
expect_equal(recessive, test_recessive)
inheritance_model <- "overdominant"
recessive <- applyInheritanceModel(experiment, inheritance_model)
test_recessive <- matrix(c(0, 0, 1, 1, 0, 1, 0, 0, 0), nrow = 3)
expect_equal(recessive, test_recessive)
# works on summarized experiment
se <- SummarizedExperiment(
assays = list(matrix(c(2, 0, 1, 1, 2, 1, 0, 2, 2), nrow = 3)),
colData = data.frame(foo = 1:3, row.names = 1:3)
)
se_dominant <- applyInheritanceModel(se, "dominant")
assay(se) <- ifelse(assay(se) == 0, 0, 1)
expect_equal(se_dominant, se)
})
test_that("getFrequencyMask", {
experiment_matrix <- matrix(
data = c(0, 2, 0, 0, 0,
0, 2, 0, 0, 0,
2, 0, 0, 0, 0,
0, 1, 1, 0, 0,
0, 0, 0, 0, 0
),
nrow = 5,
ncol = 5,
dimnames = list(
c("A*01:01", "A*02:01", "A*02:06", "A*03:01", "A*23:01"),
c("PAT1", "PAT2", "PAT3", "PAT4", "PAT5")
)
)
experiment_matrix_freq <-
getExperimentFrequencies(experiment_matrix, 2)
mask <-
getFrequencyMask(
df = experiment_matrix_freq,
lower_frequency_cutoff = 0.1,
upper_frequency_cutoff = 0.5
)
expect_equal(mask, "A*01:01")
mask <-
getFrequencyMask(
df = experiment_matrix_freq,
lower_frequency_cutoff = 0.1,
upper_frequency_cutoff = 0.51
)
expect_equal(mask, c("A*01:01", "A*02:01"))
})
test_that("filterExperimentByVariables", {
experiment <- matrix(
data = c(0, 2, 0, 0, 0,
0, 2, 0, 0, 0,
2, 0, 0, 0, 0,
0, 1, 1, 0, 0,
0, 0, 0, 0, 0),
nrow = 5,
ncol = 5,
dimnames = list(
c("A*01:01", "A*02:01", "A*02:06", "A*03:01", "A*23:01"),
c("PAT1", "PAT2", "PAT3", "PAT4", "PAT5")
)
)
experiment_filtered <- filterExperimentByVariables(experiment, c("A*01:01", "A*02:01"))
expect_equal(experiment_filtered, experiment[1:2, ])
experiment <- SummarizedExperiment::SummarizedExperiment(experiment)
metadata(experiment)$omnibus_groups <-
list(A = c("A*01:01", "A*02:01", "A*02:06", "A*03:01", "A*23:01"))
experiment_filtered <- filterExperimentByVariables(experiment, c("A*01:01", "A*02:01"))
test_experiment <- experiment[c("A*01:01", "A*02:01"), ]
expect_equal(experiment_filtered, test_experiment)
})
test_that("getExperimentPopulationMultiplicator", {
se <- SummarizedExperiment::SummarizedExperiment(
assays = list(matrix(1:5)),
metadata = list(pop_mul = 2)
)
expect_equal(
getExperimentPopulationMultiplicator(se),
2
)
mat <- matrix(1:5)
expect_equal(
getExperimentPopulationMultiplicator(mat),
NULL
)
})
test_that("isExperimentInheritanceModelApplicable", {
se <- SummarizedExperiment::SummarizedExperiment(
assays = list(matrix(1:5)),
metadata = list(inheritance_model_applicable = TRUE)
)
expect_equal(
isExperimentInheritanceModelApplicable(se),
TRUE
)
mat <- matrix(1:5)
expect_equal(
isExperimentInheritanceModelApplicable(mat),
FALSE
)
})
Genentech/MiDAS documentation built on Feb. 5, 2024, 11:52 a.m.
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context("Transforming MiDAS objects")
test_that("hlaToAAVariation", {
aa_variation <-
hlaToAAVariation(MiDAS_tut_HLA,
indels = TRUE,
unkchar = TRUE,
as_df = FALSE)
load(system.file("extdata", "test_aa_variation.Rdata", package = "midasHLA"))
expect_equal(aa_variation, test_aa_variation)
expect_error(hlaToAAVariation(MiDAS_tut_HLA, indels = "foo"),
"indels is not a flag \\(a length one logical vector\\)."
)
expect_error(hlaToAAVariation(MiDAS_tut_HLA, unkchar = "foo"),
"unkchar is not a flag \\(a length one logical vector\\)."
)
expect_error(hlaToAAVariation(MiDAS_tut_HLA, as_df = 1),
"as_df is not a flag \\(a length one logical vector\\)."
)
})
test_that("hlaToVariable", {
hla_supertypes <- hlaToVariable(MiDAS_tut_HLA, dictionary = "allele_HLA_supertype")
test_hla_supertypes <-
lapply(
MiDAS_tut_HLA[,-1],
convertAlleleToVariable,
dictionary = system.file("extdata", "Match_allele_HLA_supertype.txt", package = "midasHLA")
)
na_mask <- vapply(test_hla_supertypes, function(x) all(is.na(x)), FUN.VALUE = logical(1))
test_hla_supertypes <- test_hla_supertypes[! na_mask]
test_hla_supertypes <- do.call(cbind, test_hla_supertypes)
colnames(test_hla_supertypes) <-
paste0("supertype_", colnames(test_hla_supertypes))
test_hla_supertypes <-
cbind(MiDAS_tut_HLA[, 1, drop = FALSE], test_hla_supertypes, stringsAsFactors = FALSE)
expect_equal(hla_supertypes, test_hla_supertypes)
expect_error(
hlaToVariable(c("A*01:01", "A*02:01"), dictionary = "4digit_supertype"),
"hla_calls is not a data frame"
)
expect_error(
hlaToVariable(
hla_calls = MiDAS_tut_HLA,
dictionary = "4digit_supertype",
reduce = "yes"
),
"reduce is not a flag \\(a length one logical vector\\)."
)
expect_error(
hlaToVariable(
hla_calls = MiDAS_tut_HLA,
dictionary = "4digit_supertype",
na.value = 1:5
),
"na.value length must equal 1."
)
expect_error(
hlaToVariable(
hla_calls = MiDAS_tut_HLA,
dictionary = "4digit_supertype",
nacols.rm = "yes"
),
"nacols.rm is not a flag \\(a length one logical vector\\)."
)
})
test_that("hlaCallsToCounts", {
hla_calls <- data.frame(
ID = c("PAT1", "PAT2", "PAT3", "PAT4"),
A_1 = c("A*01", "A*02", NA, "A*01"),
A_2 = c("A*02", "A*01", "A*02", "A*01"),
stringsAsFactors = FALSE
)
hla_counts <- hlaCallsToCounts(hla_calls)
test_hla_counts <- data.frame(
ID = c("PAT1", "PAT2", "PAT3", "PAT4"),
"A*01" = c(1, 1, 0, 2),
"A*02" = c(1, 1, 1, 0),
check.names = FALSE,
stringsAsFactors = FALSE
)
expect_equal(hla_counts, test_hla_counts)
# check if non HLA data frame can be properly processed
nonhla <-
data.frame(
ID = 1:2,
A_1 = c("a1", "a2"),
A_2 = c("a2", "a2"),
stringsAsFactors = FALSE
)
nonhla_counts <- hlaCallsToCounts(nonhla, check_hla_format = FALSE)
expect_equal(nonhla_counts,
data.frame(
ID = 1:2,
a1 = c(1, 0),
a2 = c(1, 2),
stringsAsFactors = FALSE
))
expect_error(
hlaCallsToCounts(c("A*01:01", "A*02:01")),
"hla_calls is not a data frame"
)
expect_error(
hlaCallsToCounts(
hla_calls,
check_hla_format = 1
),
"check_hla_format is not a flag \\(a length one logical vector\\)."
)
})
test_that("getHlaFrequencies", {
minimal_hla_calls <- data.frame(
ID = c("P1", "P2"),
A_1 = c("A*01:01", "A*02:01"),
A_2 = c("A*02:01", "A*01:01"),
stringsAsFactors = FALSE
)
hla_freq <- getHlaFrequencies(minimal_hla_calls)
test_hla_freq <- data.frame(
allele = c("A*01:01", "A*02:01"),
Counts = c(2, 2),
Freq = c(0.5, 0.5),
row.names = NULL,
stringsAsFactors = FALSE
)
expect_equal(hla_freq, test_hla_freq)
})
test_that("getKIRFrequencies", {
kir_freq <- getKIRFrequencies(MiDAS_tut_KIR)
test_kir_freq <- data.frame(
gene = c("KIR3DL3", "KIR2DS2", "KIR2DL2", "KIR2DL3", "KIR2DP1", "KIR2DL1", "KIR3DP1", "KIR2DL4",
"KIR3DL1", "KIR3DS1", "KIR2DL5", "KIR2DS3", "KIR2DS5", "KIR2DS4", "KIR2DS1", "KIR3DL2"),
Counts = c(935, 455, 449, 849, 925, 918, 935, 935, 853, 365, 485, 294, 288, 853, 371, 935),
Freq = c(1, 0.486631016042781, 0.480213903743316, 0.908021390374332, 0.989304812834225, 0.981818181818182,
1, 1, 0.912299465240642, 0.390374331550802, 0.518716577540107, 0.314438502673797,
0.308021390374332, 0.912299465240642, 0.396791443850267, 1),
row.names = NULL,
stringsAsFactors = FALSE
)
expect_equal(kir_freq, test_kir_freq)
})
test_that("hlaToAAVariation", {
minimal_hla_calls <- data.frame(
ID = c("P1", "P2"),
A_1 = c("A*01:01", "A*02:01"),
A_2 = c("A*02:01", "A*01:01"),
stringsAsFactors = FALSE
)
aa_var <- hlaToAAVariation(minimal_hla_calls)[, 1:5]
aa_counts <- aaVariationToCounts(aa_var)
test_aa_counts <- data.frame(
ID = c("P1", "P2"),
A_44_K = c(1, 1),
A_44_R = c(1, 1),
A_62_Q = c(1, 1),
A_62_G = c(1, 1),
stringsAsFactors = FALSE, check.names = FALSE
)
expect_equal(aa_counts, test_aa_counts)
expect_error(
aaVariationToCounts(c("x", "c")),
"aa_variation is not a data frame"
)
expect_error(
aaVariationToCounts(aa_var[, -1]),
"first column of aa_variation must be named ID"
)
})
test_that("getAAFrequencies", {
minimal_hla_calls <- data.frame(
ID = c("P1", "P2"),
A_1 = c("A*01:01", "A*02:01"),
A_2 = c("A*02:01", "A*01:01"),
stringsAsFactors = FALSE
)
aa_var <- hlaToAAVariation(minimal_hla_calls)[, 1:5]
aa_freq <- getAAFrequencies(aa_var)
test_aa_freq <- data.frame(
aa_pos = c("A_44_K", "A_44_R", "A_62_Q", "A_62_G"),
Counts = c(2, 2, 2, 2),
Freq = c(0.5, 0.5, 0.5, 0.5),
row.names = NULL,
stringsAsFactors = FALSE
)
expect_equal(aa_freq, test_aa_freq)
expect_error(
aaVariationToCounts(c("x", "c")),
"aa_variation is not a data frame"
)
expect_error(
aaVariationToCounts(aa_var[, -1]),
"first column of aa_variation must be named ID"
)
})
test_that("formatResults", {
res <- tibble(term = c("A*01:01", "A*02:01"),
estimate = c(5, -6),
p.value = c(0.05, 0.1))
restab <- formatResults(res,
filter_by = c("p.value <= 0.05", "estimate > 0"),
arrange_by = c("p.value * estimate"),
select_cols = c("allele" = "term", "p.value"),
format = "html",
header = "informative header")
correct_tab <-
dplyr::filter(res, .data[["p.value"]] <= 0.05, .data[["estimate"]] > 0)
correct_tab <-
dplyr::arrange(correct_tab, .data[["p.value"]] * .data[["estimate"]])
correct_tab <-
dplyr::select(correct_tab, "allele" = .data[["term"]], .data[["p.value"]])
correct_tab <-
knitr::kable(correct_tab, format = "html", format.args = list(digits = 4, scientific = -3))
correct_tab <-
kableExtra::add_header_above(correct_tab, header = c("informative header" = 2))
correct_tab <-
kableExtra::kable_styling(correct_tab,
bootstrap_options = c("striped", "hover", "condensed"))
correct_tab <-
kableExtra::scroll_box(correct_tab, width = "100%", height = "400px")
expect_equal(restab, correct_tab)
expect_error(formatResults(res, filter_by = 1),
"filter_by is not a character vector")
expect_error(formatResults(res, arrange_by = 1),
"arrange_by is not a character vector")
expect_error(formatResults(res, select_cols = 1),
"select_cols is not a character vector")
expect_error(formatResults(res, format = 1),
"format is not a string \\(a length one character vector\\).")
expect_error(formatResults(res, format = "markdown"),
"format should be one of \"html\", \"latex\".")
expect_error(formatResults(res, format ="html", header = 1),
"header is not a string \\(a length one character vector\\) or NULL.")
expect_error(formatResults(res, format ="html", scroll_box_height = 1),
"scroll_box_height is not a string \\(a length one character vector\\).")
})
test_that("kableResults", {
midas <- prepareMiDAS(hla_calls = MiDAS_tut_HLA,
colData = MiDAS_tut_pheno,
experiment = "hla_alleles")
object <- lm(disease ~ term, data = midas)
res <- runMiDAS(object, inheritance_model = "additive", experiment = "hla_alleles")
res_kable <- kableResults(res)
res_kable_test <- formatResults(res,
filter_by = "p.value <= 1",
arrange_by = "p.value",
select_cols = c("allele",
"p.value",
"p.adjusted",
"estimate",
"std.error",
"conf.low",
"conf.high",
"statistic",
"Ntotal",
"Ntotal [%]" = "Ntotal.percent",
"N(disease=0)",
"N(disease=0) [%]" = "N(disease=0).percent",
"N(disease=1)",
"N(disease=1) [%]" = "N(disease=1).percent"
),
format = "html",
header = "MiDAS analysis results"
)
expect_equal(res_kable, res_kable_test)
expect_error(kableResults(LETTERS), "results is not a data frame")
expect_error(kableResults(res, 1:5), "colnames is not a character vector or NULL.")
expect_error(kableResults(res, pvalue_cutoff = "a"),
"pvalue_cutoff is not a number \\(a length one numeric vector\\) or NULL."
)
expect_error(kableResults(res, format = 1),
"format is not a string \\(a length one character vector\\)."
)
expect_error(kableResults(res, format = "foo"),
"format should be one of \"html\", \"latex\"."
)
expect_error(
kableResults(res, c("foo", "bar")),
"colnames should match values \"allele\", \"p.value\", \"p.adjusted\", \"estimate\", \"std.error\", \"conf.low\", \"conf.high\", \"statistic\", \"Ntotal\", \"Ntotal.percent\", \"N\\(disease=0\\)\", \"N\\(disease=0\\).percent\", \"N\\(disease=1\\)\", \"N\\(disease=1\\).percent\"."
)
expect_error(kableResults(res, scroll_box_height = 1),
"scroll_box_height is not a string \\(a length one character vector\\)."
)
})
test_that("countsToVariables", {
kir_haplotypes <- countsToVariables(MiDAS_tut_KIR[1:2, ], "kir_haplotypes")
kir_haplotypes_test <- data.frame(
ID = c("C001", "C002"),
cenAA = c(1, 1),
cenBB = c(0, 0),
cenAB = c(0, 0),
telAA = c(1, 1),
telBB = c(0, 0),
telAB = c(0, 0),
stringsAsFactors = FALSE
)
expect_equal(kir_haplotypes, kir_haplotypes_test)
# works with a dictionary data frame
dictionary <- data.frame(
Name = c("cenAA", "cenAB", "telAA"),
Expression = c(
"KIR2DL3 & ! KIR2DL2 & ! KIR2DS2",
"! (KIR2DL2 & ! KIR2DL3) & ! (KIR2DL3 & ! KIR2DL2 & ! KIR2DS2)",
"! KIR2DS1 & ! KIR3DS1"
),
stringsAsFactors = FALSE
)
kir_haplotypes <- countsToVariables(MiDAS_tut_KIR[1:2, ], dictionary)
expect_equal(kir_haplotypes,
kir_haplotypes_test[, c("ID", "cenAA", "cenAB", "telAA")]
)
expect_error(
countsToVariables(iris),
"first column in counts must be named 'ID'"
)
expect_error(
countsToVariables(MiDAS_tut_KIR, na.value = 1:2),
"na.value length must equal 1."
)
expect_error(
countsToVariables(MiDAS_tut_KIR, nacols.rm = 1),
"nacols.rm is not a flag \\(a length one logical vector\\)."
)
expect_error(
countsToVariables(MiDAS_tut_KIR, dictionary = "foo"),
"Path 'foo' does not exist"
)
expect_error(
countsToVariables(MiDAS_tut_KIR, dictionary = c("foo", "bar")),
"dictionary is not a data frame"
)
})
test_that("getHlaKirInteractions", {
hla_kir <- getHlaKirInteractions(MiDAS_tut_HLA, MiDAS_tut_KIR)
load(system.file("extdata", "test_hla_kir_interactions.Rdata", package = "midasHLA"))
expect_equal(hla_kir, test_hla_kir_interactions)
expect_error(
getHlaKirInteractions(MiDAS_tut_HLA, MiDAS_tut_KIR, interactions_dict = 1),
"interactions_dict is not a string \\(a length one character vector\\)."
)
fake_kir_counts <- MiDAS_tut_KIR
fake_kir_counts[, 1] <- paste0("foo", 1:nrow(fake_kir_counts))
expect_error(
getHlaKirInteractions(MiDAS_tut_HLA, fake_kir_counts),
"IDs in hla_calls doesn't match IDs in kir_calls"
)
fake_kir_counts <- MiDAS_tut_KIR
fake_kir_counts[1:5, 1] <- paste0("foo", 1:5)
expect_warning(
getHlaKirInteractions(MiDAS_tut_HLA, fake_kir_counts),
"995 IDs in hla_calls matched IDs in kir_calls"
)
})
test_that("filterExperimentByFrequency", {
# filtering works as expected for fractions
experiment <- MiDAS_tut_object[["hla_alleles"]]
lower_frequency_cutoff <- 0.50
upper_frequency_cutoff <- 0.90
experiment_filtered <- filterExperimentByFrequency(
experiment = experiment,
lower_frequency_cutoff = lower_frequency_cutoff,
upper_frequency_cutoff = upper_frequency_cutoff
)
expected_vars <- c("DPA1*01:03", "DRA*01:01")
expect_equal(experiment_filtered, experiment[expected_vars, ])
# filtering works as expected for counts
experiment <- MiDAS_tut_object[["hla_alleles"]]
lower_frequency_cutoff <- 8
upper_frequency_cutoff <- 10
experiment_filtered <- filterExperimentByFrequency(
experiment = experiment,
lower_frequency_cutoff = lower_frequency_cutoff,
upper_frequency_cutoff = upper_frequency_cutoff
)
# order of rows somehow depends on the R version...
expected_vars <-
c("B*27:02",
"B*41:01",
"B*41:02",
"C*08:03",
"DPA1*02:06",
"DRB1*01:03",
"DRB1*08:04")
expected_vars <- expected_vars[order(expected_vars)]
experiment_filtered <-
experiment_filtered[order(rownames(experiment_filtered)),]
expect_equal(experiment_filtered, experiment[expected_vars, ])
# filtering works as expected for boundry conditions NULL, NULL
experiment <- MiDAS_tut_object[["hla_alleles"]]
lower_frequency_cutoff <- NULL
upper_frequency_cutoff <- NULL
experiment_filtered <- filterExperimentByFrequency(
experiment = experiment,
lower_frequency_cutoff = lower_frequency_cutoff,
upper_frequency_cutoff = upper_frequency_cutoff
)
expect_equal(experiment_filtered, experiment)
# filtering works as expected for boundry conditions 0, 0
experiment <- MiDAS_tut_object[["hla_alleles"]]
lower_frequency_cutoff <- 0
upper_frequency_cutoff <- 0
experiment_filtered <- filterExperimentByFrequency(
experiment = experiment,
lower_frequency_cutoff = lower_frequency_cutoff,
upper_frequency_cutoff = upper_frequency_cutoff
)
expected_vars <- character(0L)
expect_equal(experiment_filtered, experiment[expected_vars, ])
# filtering works as expected for boundry conditions 1, 1
experiment <- MiDAS_tut_object[["hla_alleles"]]
lower_frequency_cutoff <- 1
upper_frequency_cutoff <- 1
experiment_filtered <- filterExperimentByFrequency(
experiment = experiment,
lower_frequency_cutoff = lower_frequency_cutoff,
upper_frequency_cutoff = upper_frequency_cutoff
)
expected_vars <- character(0L)
expect_equal(experiment_filtered, experiment[expected_vars, ])
# omnibus groups are filtred correctly
experiment <- MiDAS_tut_object[["hla_aa"]]
lower_frequency_cutoff <- 0.86
upper_frequency_cutoff <- 0.87
experiment_filtered <- filterExperimentByFrequency(
experiment = experiment,
lower_frequency_cutoff = lower_frequency_cutoff,
upper_frequency_cutoff = upper_frequency_cutoff
)
test_experiment <- experiment[c("C_35_R", "C_309_V"), ]
expect_equal(experiment_filtered, test_experiment)
# experiment must be of type integer
experiment <- matrix(LETTERS)
lower_frequency_cutoff <- 0.1
upper_frequency_cutoff <- 0.5
expect_error(
filterExperimentByFrequency(
experiment = experiment,
lower_frequency_cutoff = lower_frequency_cutoff,
upper_frequency_cutoff = upper_frequency_cutoff
),
"Frequency filtration does not support provided experiment."
)
# carrier_frequency must be a string
experiment <- MiDAS_tut_object[["hla_alleles"]]
inheritance_model <- 1
lower_frequency_cutoff <- "foo"
upper_frequency_cutoff <- 0.5
expect_error(
filterExperimentByFrequency(
experiment = experiment,
carrier_frequency = 1,
lower_frequency_cutoff = lower_frequency_cutoff,
upper_frequency_cutoff = upper_frequency_cutoff
),
"carrier_frequency is not a flag \\(a length one logical vector\\)."
)
# lower_frequency_cutof must be a number
experiment <- MiDAS_tut_object[["hla_alleles"]]
lower_frequency_cutoff <- "foo"
upper_frequency_cutoff <- 0.5
expect_error(
filterExperimentByFrequency(
experiment = experiment,
lower_frequency_cutoff = lower_frequency_cutoff,
upper_frequency_cutoff = upper_frequency_cutoff
),
"lower_frequency_cutoff is not a number \\(a length one numeric vector\\)."
)
# lower_frequency_cutof must be positive
experiment <- MiDAS_tut_object[["hla_alleles"]]
lower_frequency_cutoff <- -1
upper_frequency_cutoff <- 0.5
expect_error(
filterExperimentByFrequency(
experiment = experiment,
lower_frequency_cutoff = lower_frequency_cutoff,
upper_frequency_cutoff = upper_frequency_cutoff
),
"lower_frequency_cutoff must be a number greater than 0."
)
# upper_frequency_cutoff must be a number
experiment <- MiDAS_tut_object[["hla_alleles"]]
lower_frequency_cutoff <- 0.5
upper_frequency_cutoff <- "foo"
expect_error(
filterExperimentByFrequency(
experiment = experiment,
lower_frequency_cutoff = lower_frequency_cutoff,
upper_frequency_cutoff = upper_frequency_cutoff
),
"upper_frequency_cutoff is not a number \\(a length one numeric vector\\)."
)
# upper_frequency_cutoff must be positive
experiment <- MiDAS_tut_object[["hla_alleles"]]
lower_frequency_cutoff <- 0
upper_frequency_cutoff <- -1
expect_error(
filterExperimentByFrequency(
experiment = experiment,
lower_frequency_cutoff = lower_frequency_cutoff,
upper_frequency_cutoff = upper_frequency_cutoff
),
"upper_frequency_cutoff must be a number greater than 0."
)
# lower_frequency_cutoff is lower than upper_frequency_cutoff
experiment <- MiDAS_tut_object[["hla_alleles"]]
lower_frequency_cutoff <- 5
upper_frequency_cutoff <- 1
expect_error(
filterExperimentByFrequency(
experiment = experiment,
lower_frequency_cutoff = lower_frequency_cutoff,
upper_frequency_cutoff = upper_frequency_cutoff
),
"lower_frequency_cutoff cannot be higher than upper_frequency_cutoff."
)
# Both lower_frequency_cutoff and upper_frequency_cutoff have to be either frequencies or counts
experiment <- MiDAS_tut_object[["hla_alleles"]]
lower_frequency_cutoff <- 0.5
upper_frequency_cutoff <- 2
expect_error(
filterExperimentByFrequency(
experiment = experiment,
lower_frequency_cutoff = lower_frequency_cutoff,
upper_frequency_cutoff = upper_frequency_cutoff
),
"Both lower_frequency_cutoff and upper_frequency_cutoff have to be either frequencies or counts."
)
})
test_that("getExperimentFrequencies", {
experiment_matrix <- matrix(
data = c(0, 2, 0, 0, 0,
0, 2, 0, 0, 0,
2, 0, 0, 0, 0,
0, 1, 1, 0, 0,
0, 0, 0, 0, 0
),
nrow = 5,
ncol = 5,
dimnames = list(
c("A*01:01", "A*02:01", "A*02:06", "A*03:01", "A*23:01"),
c("PAT1", "PAT2", "PAT3", "PAT4", "PAT5")
)
)
experiment_se <- SummarizedExperiment::SummarizedExperiment(
assays = list(experiment_matrix),
metadata = list(
inheritance_model_applicable = TRUE,
pop_mul = 2,
omnibus_groups = NULL
)
)
# allele frequecny
experiment_freq_test <- data.frame(
term = c("A*01:01", "A*02:01", "A*02:06", "A*03:01", "A*23:01"),
Counts = c(2, 5, 1, 0, 0),
Freq = c(0.2, 0.5, 0.1, 0, 0),
row.names = NULL,
stringsAsFactors = FALSE
)
# matrix
expect_equal(getExperimentFrequencies(experiment_matrix, 2), experiment_freq_test)
# se
experiment_freq <-
getExperimentFrequencies(experiment_se)
expect_equal(experiment_freq, experiment_freq_test)
# carrier frequency
experiment_freq <-
getExperimentFrequencies(experiment_se, carrier_frequency = TRUE)
experiment_freq_test <- data.frame(
term = c("A*01:01", "A*02:01", "A*02:06", "A*03:01", "A*23:01"),
Counts = c(1, 3, 1, 0, 0),
Freq = c(0.2, 0.6, 0.2, 0, 0),
row.names = NULL,
stringsAsFactors = FALSE
)
expect_equal(experiment_freq, experiment_freq_test)
expect_error(
getExperimentFrequencies(matrix(LETTERS)),
"values in experiment are not counts \\(a positive integers\\) or zeros."
)
expect_error(getExperimentFrequencies(experiment_matrix),
"pop_mul is not a number \\(a length one numeric vector\\)."
)
expect_error(getExperimentFrequencies(experiment_matrix, 1, carrier_frequency = 1),
"carrier_frequency is not a flag \\(a length one logical vector\\).")
expect_error(getExperimentFrequencies(experiment_matrix, 1, ref = "foo"),
"ref is not a data frame")
})
test_that("applyInheritanceModel", {
experiment <- matrix(c(2, 0, 1, 1, 2, 1, 0, 2, 2), nrow = 3)
inheritance_model <- "additive"
additive <- applyInheritanceModel(experiment, inheritance_model)
expect_equal(experiment, additive)
inheritance_model <- "dominant"
dominant <- applyInheritanceModel(experiment, inheritance_model)
test_dominant <- matrix(c(1, 0, 1, 1, 1, 1, 0, 1, 1), nrow = 3)
expect_equal(dominant, test_dominant)
inheritance_model <- "recessive"
recessive <- applyInheritanceModel(experiment, inheritance_model)
test_recessive <- matrix(c(1, 0, 0, 0, 1, 0, 0, 1, 1), nrow = 3)
expect_equal(recessive, test_recessive)
inheritance_model <- "overdominant"
recessive <- applyInheritanceModel(experiment, inheritance_model)
test_recessive <- matrix(c(0, 0, 1, 1, 0, 1, 0, 0, 0), nrow = 3)
expect_equal(recessive, test_recessive)
# works on summarized experiment
se <- SummarizedExperiment(
assays = list(matrix(c(2, 0, 1, 1, 2, 1, 0, 2, 2), nrow = 3)),
colData = data.frame(foo = 1:3, row.names = 1:3)
)
se_dominant <- applyInheritanceModel(se, "dominant")
assay(se) <- ifelse(assay(se) == 0, 0, 1)
expect_equal(se_dominant, se)
})
test_that("getFrequencyMask", {
experiment_matrix <- matrix(
data = c(0, 2, 0, 0, 0,
0, 2, 0, 0, 0,
2, 0, 0, 0, 0,
0, 1, 1, 0, 0,
0, 0, 0, 0, 0
),
nrow = 5,
ncol = 5,
dimnames = list(
c("A*01:01", "A*02:01", "A*02:06", "A*03:01", "A*23:01"),
c("PAT1", "PAT2", "PAT3", "PAT4", "PAT5")
)
)
experiment_matrix_freq <-
getExperimentFrequencies(experiment_matrix, 2)
mask <-
getFrequencyMask(
df = experiment_matrix_freq,
lower_frequency_cutoff = 0.1,
upper_frequency_cutoff = 0.5
)
expect_equal(mask, "A*01:01")
mask <-
getFrequencyMask(
df = experiment_matrix_freq,
lower_frequency_cutoff = 0.1,
upper_frequency_cutoff = 0.51
)
expect_equal(mask, c("A*01:01", "A*02:01"))
})
test_that("filterExperimentByVariables", {
experiment <- matrix(
data = c(0, 2, 0, 0, 0,
0, 2, 0, 0, 0,
2, 0, 0, 0, 0,
0, 1, 1, 0, 0,
0, 0, 0, 0, 0),
nrow = 5,
ncol = 5,
dimnames = list(
c("A*01:01", "A*02:01", "A*02:06", "A*03:01", "A*23:01"),
c("PAT1", "PAT2", "PAT3", "PAT4", "PAT5")
)
)
experiment_filtered <- filterExperimentByVariables(experiment, c("A*01:01", "A*02:01"))
expect_equal(experiment_filtered, experiment[1:2, ])
experiment <- SummarizedExperiment::SummarizedExperiment(experiment)
metadata(experiment)$omnibus_groups <-
list(A = c("A*01:01", "A*02:01", "A*02:06", "A*03:01", "A*23:01"))
experiment_filtered <- filterExperimentByVariables(experiment, c("A*01:01", "A*02:01"))
test_experiment <- experiment[c("A*01:01", "A*02:01"), ]
expect_equal(experiment_filtered, test_experiment)
})
test_that("getExperimentPopulationMultiplicator", {
se <- SummarizedExperiment::SummarizedExperiment(
assays = list(matrix(1:5)),
metadata = list(pop_mul = 2)
)
expect_equal(
getExperimentPopulationMultiplicator(se),
2
)
mat <- matrix(1:5)
expect_equal(
getExperimentPopulationMultiplicator(mat),
NULL
)
})
test_that("isExperimentInheritanceModelApplicable", {
se <- SummarizedExperiment::SummarizedExperiment(
assays = list(matrix(1:5)),
metadata = list(inheritance_model_applicable = TRUE)
)
expect_equal(
isExperimentInheritanceModelApplicable(se),
TRUE
)
mat <- matrix(1:5)
expect_equal(
isExperimentInheritanceModelApplicable(mat),
FALSE
)
})
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