567 lines
19 KiB
R
567 lines
19 KiB
R
#' These tests cover the validation logic owned by supeRglue bambu before bambu
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#' itself is invoked: BAM/alias argument checks, sample-sheet alignment,
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#' transcriptome mode selection, and NDR handling.
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#'
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#' NOTE: Annotation/reference preparation is handled by Python
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#' (bin/workflow_glue/prepare_annotation_reference.py) with pytest coverage.
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#' These tests focus on bambu-specific validation and integration.
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# Workflow requires explicit BAM paths and aliases.
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# Fail fast with clear error rather than passing invalid inputs to bambu.
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testthat::test_that("BAM inputs required", {
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args <- list(
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annotation = "annotation.gtf",
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genome = "genome.fa",
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out_dir = tempfile("bambu-out-"),
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bams = NULL,
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aliases = NULL,
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transcriptome_mode = "discover",
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ndr = NULL
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)
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testthat::expect_error(
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workflow_glue_r_normalise_args(args, bambu_arg_spec()),
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"Missing required arguments: --bams"
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)
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args$bams <- "sampleA.bam"
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testthat::expect_error(
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workflow_glue_r_normalise_args(args, bambu_arg_spec()),
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"Missing required arguments: --aliases"
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)
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args$aliases <- "sampleA"
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normalised <- NULL
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testthat::expect_silent(normalised <- workflow_glue_r_normalise_args(args, bambu_arg_spec()))
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testthat::expect_identical(normalised$threads, 1L)
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})
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# transcriptome_mode must be "discover" or "fixed_annotation".
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# NDR (Novel Discovery Rate) must be between 0 and 1 when provided.
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testthat::test_that("invalid discovery settings rejected", {
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args <- list(
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annotation = "annotation.gtf",
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genome = "genome.fa",
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out_dir = tempfile("bambu-out-"),
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bams = "sampleA.bam",
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aliases = "sampleA",
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transcriptome_mode = "novel",
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ndr = NULL
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)
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testthat::expect_error(
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workflow_glue_r_normalise_args(args, bambu_arg_spec()),
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"transcriptome_mode must be one of"
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)
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args$transcriptome_mode <- "discover"
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args$ndr <- -0.01
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testthat::expect_error(
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workflow_glue_r_normalise_args(args, bambu_arg_spec()),
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"NDR .* must be between 0 and 1"
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)
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args$ndr <- 1.01
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testthat::expect_error(
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workflow_glue_r_normalise_args(args, bambu_arg_spec()),
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"NDR .* must be between 0 and 1"
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)
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args$ndr <- 0
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testthat::expect_silent(workflow_glue_r_normalise_args(args, bambu_arg_spec()))
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args$ndr <- 1
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testthat::expect_silent(workflow_glue_r_normalise_args(args, bambu_arg_spec()))
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args$ndr <- NULL
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args$threads <- "2"
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normalised <- workflow_glue_r_normalise_args(args, bambu_arg_spec())
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testthat::expect_identical(normalised$threads, 2L)
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})
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# Fail fast if --bams is empty rather than passing empty input to bambu.
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testthat::test_that("empty BAM list rejected", {
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args <- list(
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bams = "",
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aliases = "",
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sample_sheet = NULL
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)
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testthat::expect_error(
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bambu_resolve_inputs(args),
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"No BAM files were provided in --bams"
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)
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})
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# Sample aliases must be unique and explicitly specified.
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# Sample sheets must have an 'alias' column with unique values that match BAM inputs.
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testthat::test_that("unique sample aliases required", {
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args <- list(
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bams = "sampleA.bam,sampleB.bam",
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aliases = "sampleA,sampleA",
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sample_sheet = NULL
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)
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testthat::expect_error(
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bambu_resolve_inputs(args),
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"BAM aliases must be unique"
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)
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args$aliases <- "sampleA"
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testthat::expect_error(
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bambu_resolve_inputs(args),
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"Provide one alias per BAM in --bams"
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)
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missing_alias_sheet <- tempfile(fileext = ".csv")
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writeLines(
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paste(
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"condition",
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"control",
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sep = "\n"
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),
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missing_alias_sheet
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)
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args <- list(
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bams = "sampleA.bam",
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aliases = "sampleA",
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sample_sheet = missing_alias_sheet
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)
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testthat::expect_error(
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bambu_resolve_inputs(args),
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"Sample sheet must contain an 'alias' column"
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)
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duplicate_alias_sheet <- tempfile(fileext = ".csv")
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writeLines(
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paste(
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"alias,condition",
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"sampleA,control",
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"sampleA,treated",
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sep = "\n"
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),
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duplicate_alias_sheet
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)
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args$sample_sheet <- duplicate_alias_sheet
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testthat::expect_error(
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bambu_resolve_inputs(args),
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"Sample sheet aliases must be unique"
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)
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})
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# Sample sheet rows must align with BAM file order.
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# If sheet lists samples in different order than CLI aliases, reorder sheet to match.
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# If sheet is missing aliases found in BAMs, fail.
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testthat::test_that("sample sheet reordered to match BAMs", {
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sample_sheet <- tempfile(fileext = ".csv")
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writeLines(
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paste(
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"alias,condition",
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"sampleB,treated",
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"sampleA,control",
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sep = "\n"
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),
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sample_sheet
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)
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args <- list(
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bams = "sampleA.aligned.sorted.bam,sampleB.bam",
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aliases = "sampleA,sampleB",
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sample_sheet = sample_sheet
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)
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resolved <- bambu_resolve_inputs(args)
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testthat::expect_equal(resolved$aliases, c("sampleA", "sampleB"))
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testthat::expect_equal(resolved$sample_df$alias, c("sampleA", "sampleB"))
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testthat::expect_s4_class(resolved$reads, "BamFileList")
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bad_sheet <- tempfile(fileext = ".csv")
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writeLines(
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paste(
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"alias,condition",
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"sampleA,control",
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sep = "\n"
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),
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bad_sheet
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)
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args$sample_sheet <- bad_sheet
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testthat::expect_error(
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bambu_resolve_inputs(args),
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"Sample sheet is missing alias rows"
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)
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})
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# transcriptome_mode="discover" → bambu(discovery=TRUE, NDR=value)
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# transcriptome_mode="fixed_annotation" → bambu(discovery=FALSE, no NDR param)
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testthat::test_that("transcriptome mode mapped to bambu args", {
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annotation_obj <- structure(list(annotation = TRUE), class = "mockAnnotation")
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discover_args <- list(
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genome = "genome.fa",
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threads = 3,
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transcriptome_mode = "discover",
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ndr = 0.2
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)
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discover <- bambu_build_args(discover_args, reads = "sample.bam", annotation_obj = annotation_obj)
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testthat::expect_true(discover$discovery)
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testthat::expect_equal(discover$NDR, 0.2)
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testthat::expect_equal(discover$ncore, 3L)
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testthat::expect_true(discover$lowMemory)
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testthat::expect_equal(discover$yieldSize, 250000L)
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auto_ndr_args <- list(
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genome = "genome.fa",
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threads = 2,
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transcriptome_mode = "discover",
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ndr = NULL
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)
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auto_ndr <- bambu_build_args(auto_ndr_args, reads = "sample.bam", annotation_obj = annotation_obj)
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testthat::expect_true(auto_ndr$discovery)
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testthat::expect_false("NDR" %in% names(auto_ndr))
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testthat::expect_equal(auto_ndr$yieldSize, 250000L)
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fixed_args <- list(
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genome = "genome.fa",
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threads = 1,
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transcriptome_mode = "fixed_annotation",
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ndr = NULL
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)
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fixed <- bambu_build_args(fixed_args, reads = "sample.bam", annotation_obj = annotation_obj)
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testthat::expect_false(fixed$discovery)
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testthat::expect_false("NDR" %in% names(fixed))
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testthat::expect_true(fixed$lowMemory)
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testthat::expect_equal(fixed$yieldSize, 250000L)
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})
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###
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# Output serialization
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#
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# bambu returns Bioconductor objects with metadata columns that may be list-like
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# or range-backed. Check that these are flattened or simplified appropriately
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# when written to TSV.
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# List-valued metadata columns (e.g., eqClassById) must be collapsed to strings for TSV.
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testthat::test_that("list columns flattened for TSV output", {
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df <- data.frame(name = c("a", "b"), stringsAsFactors = FALSE)
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df$list_col <- I(list(c("x", "y"), "z"))
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normalised <- workflow_glue_r_normalise_tsv_df(df)
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testthat::expect_equal(normalised$list_col, c("x;y", "z"))
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})
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# Verify all expected output files are created with correct structure.
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testthat::test_that("bambu outputs written correctly", {
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out_dir <- tempfile("bambu-write-")
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dir.create(out_dir)
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sample_names <- c("sampleA", "sampleB")
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base_se <- make_test_tx_se(sample_names = sample_names)
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row_ranges <- make_test_bambu_row_ranges(out_dir)
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se <- SummarizedExperiment::SummarizedExperiment(
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assays = SummarizedExperiment::assays(base_se),
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rowRanges = row_ranges
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)
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gene_se <- make_test_gene_se(sample_names = sample_names)
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sample_df <- data.frame(alias = sample_names, stringsAsFactors = FALSE)
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argv <- list(out_dir = out_dir, transcriptome_mode = "discover", ndr = 0.15)
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qc_stats <- list(
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samples = 2,
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total_transcripts_before_filter = 4,
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total_genes_before_filter = 2,
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total_transcripts_after_filter = 4,
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total_genes_after_filter = 2,
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transcripts_filtered = 0,
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median_library_size = 400,
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min_library_size = 390,
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max_library_size = 410,
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median_transcripts_detected = 4
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)
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bambu_write_outputs(
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se,
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gene_se,
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sample_df,
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argv,
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qc_stats
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)
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testthat::expect_true(file.exists(file.path(out_dir, "transcripts.gtf")))
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testthat::expect_true(file.exists(file.path(out_dir, "transcript_counts.tsv")))
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testthat::expect_true(file.exists(file.path(out_dir, "gene_counts.tsv")))
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testthat::expect_true(file.exists(file.path(out_dir, "bambu_qc_stats.json")))
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tx_meta <- utils::read.delim(
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file.path(out_dir, "transcript_metadata.tsv"),
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check.names = FALSE,
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stringsAsFactors = FALSE
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)
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tx_counts <- utils::read.delim(
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file.path(out_dir, "transcript_counts.tsv"),
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check.names = FALSE,
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stringsAsFactors = FALSE
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)
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testthat::expect_true("eqClassById" %in% names(tx_meta))
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testthat::expect_equal(tx_meta$eqClassById[[1]], "1;2")
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testthat::expect_true(all(c("TXNAME", "sampleA", "sampleB") %in% names(tx_counts)))
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})
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# Transcript filtering edge cases
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testthat::test_that("zero full-length count transcripts filtered", {
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se <- make_test_tx_se(sample_names = c("sampleA", "sampleB"))
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# Set tx1 to have full-length counts, tx2 to have none (will be filtered)
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counts <- SummarizedExperiment::assay(se, "counts")
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counts[1, ] <- c(10, 8)
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counts[2, ] <- c(5, 3)
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counts[3, ] <- c(0, 0)
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counts[4, ] <- c(0, 0)
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SummarizedExperiment::assay(se, "counts", withDimnames = FALSE) <- counts
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full_length <- matrix(0, nrow = 4, ncol = 2)
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full_length[1, ] <- c(5, 4)
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SummarizedExperiment::assays(se, withDimnames = FALSE)[["fullLengthCounts"]] <- full_length
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result <- bambu_filter_transcripts(se)
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testthat::expect_equal(nrow(result$se), 1)
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testthat::expect_equal(result$qc_stats$transcripts_filtered, 3)
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})
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testthat::test_that("filters on counts when no fullLengthCounts assay", {
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se <- make_test_tx_se(sample_names = c("sampleA", "sampleB"))
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# Set some transcripts with counts
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counts <- SummarizedExperiment::assay(se, "counts")
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counts[1, ] <- c(10, 8)
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counts[2, ] <- c(5, 3)
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counts[3, ] <- c(0, 0)
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counts[4, ] <- c(0, 0)
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SummarizedExperiment::assay(se, "counts", withDimnames = FALSE) <- counts
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# Remove fullLengthCounts assay entirely so it falls back to counts
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assay_list <- SummarizedExperiment::assays(se)
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assay_list[["fullLengthCounts"]] <- NULL
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SummarizedExperiment::assays(se, withDimnames = FALSE) <- assay_list
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result <- bambu_filter_transcripts(se)
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testthat::expect_equal(nrow(result$se), 2)
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testthat::expect_equal(result$qc_stats$transcripts_filtered, 2)
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})
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testthat::test_that("error when all transcripts filtered", {
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se <- make_test_tx_se(sample_names = c("sampleA", "sampleB"))
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# All transcripts have zero counts
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SummarizedExperiment::assay(se, "counts", withDimnames = FALSE) <- matrix(0, nrow = 4, ncol = 2)
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SummarizedExperiment::assays(se, withDimnames = FALSE)[["fullLengthCounts"]] <- matrix(0, nrow = 4, ncol = 2)
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testthat::expect_error(
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bambu_filter_transcripts(se),
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"All transcripts have zero counts after filtering"
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)
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})
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testthat::test_that("QC stats match filtered results", {
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se <- make_test_tx_se(sample_names = c("sampleA", "sampleB"))
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# Set one transcript with counts, rest without
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counts <- matrix(0, nrow = 4, ncol = 2)
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counts[1, ] <- c(10, 8)
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SummarizedExperiment::assay(se, "counts", withDimnames = FALSE) <- counts
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full_length <- matrix(0, nrow = 4, ncol = 2)
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full_length[1, ] <- c(5, 4)
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SummarizedExperiment::assays(se, withDimnames = FALSE)[["fullLengthCounts"]] <- full_length
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result <- bambu_filter_transcripts(se)
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testthat::expect_equal(result$qc_stats$total_transcripts_after_filter, nrow(result$se))
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testthat::expect_equal(result$qc_stats$transcripts_filtered, 3)
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})
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# Contract test: after transcript filtering, the filtered object must remain
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# acceptable input for gene-level aggregation.
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testthat::test_that("bambu_filter_transcripts contract with transcriptToGeneExpression", {
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fixture_dir <- tempfile("bambu-filter-contract-")
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dir.create(fixture_dir)
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base_se <- make_test_tx_se(sample_names = "sampleA")
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se <- SummarizedExperiment::SummarizedExperiment(
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assays = SummarizedExperiment::assays(base_se),
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rowRanges = make_test_bambu_row_ranges(fixture_dir)
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)
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full_length <- matrix(
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c(5, 4, 0, 0),
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nrow = nrow(se),
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ncol = ncol(se),
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dimnames = dimnames(SummarizedExperiment::assays(se)$counts)
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)
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SummarizedExperiment::assays(se, withDimnames = FALSE)[["fullLengthCounts"]] <- full_length
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S4Vectors::metadata(se)$incompatibleCounts <- data.table::data.table(
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GENEID = "gene2",
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sampleA = 7L
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)
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filtered <- bambu_filter_transcripts(se)
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testthat::expect_true(all(SummarizedExperiment::rowData(filtered$se)$GENEID == "gene1"))
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testthat::expect_equal(
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unique(S4Vectors::metadata(filtered$se)$incompatibleCounts$GENEID),
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character(0)
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)
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testthat::expect_error(bambu::transcriptToGeneExpression(filtered$se), NA)
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})
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###
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# CLI integration tests
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#
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# End-to-end tests with real bambu library (not mocked):
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# - Build BAMs from committed fixtures (reference.fa, annotation.gtf, reads.fastq)
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# - Run `supeRglue bambu` with --bams input
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# - Verify output files exist and contain data for downstream workflow steps
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testthat::test_that("CLI single BAM in bams input with fixed annotation", {
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fixture_dir <- tempfile("bambu-cli-")
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dir.create(fixture_dir)
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reference <- workflow_glue_r_fixture("bambu", "reference.fa")
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annotation <- workflow_glue_r_fixture("bambu", "annotation.gtf")
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reads <- workflow_glue_r_fixture("bambu", "reads.fastq")
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sample_sheet <- workflow_glue_r_fixture("bambu", "sample_sheet.csv")
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bam_path <- expect_bam_fixture_built(reference, reads, fixture_dir, alias = "sampleA")
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out_dir <- file.path(fixture_dir, "out")
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result <- run_rscript(
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"supeRglue",
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c(
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"bambu",
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"--bams", bam_path,
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"--aliases", "sampleA",
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"--sample_sheet", sample_sheet,
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"--annotation", annotation,
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"--genome", reference,
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"--transcriptome_mode", "fixed_annotation",
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"--threads", "1",
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"--out_dir", out_dir
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)
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)
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testthat::expect_equal(
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result$status,
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0L,
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info = paste(result$output, collapse = "\n")
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)
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testthat::expect_true(file.exists(file.path(out_dir, "transcripts.gtf")))
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testthat::expect_true(file.exists(file.path(out_dir, "transcript_counts.tsv")))
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testthat::expect_true(file.exists(file.path(out_dir, "gene_counts.tsv")))
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testthat::expect_true(file.exists(file.path(out_dir, "samples.csv")))
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tx_counts <- utils::read.delim(
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file.path(out_dir, "transcript_counts.tsv"),
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check.names = FALSE,
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stringsAsFactors = FALSE
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)
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gene_counts <- utils::read.delim(
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file.path(out_dir, "gene_counts.tsv"),
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check.names = FALSE,
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stringsAsFactors = FALSE
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)
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testthat::expect_gt(nrow(tx_counts), 0)
|
|
testthat::expect_gt(nrow(gene_counts), 0)
|
|
})
|
|
|
|
testthat::test_that("CLI bams input preserves sample order", {
|
|
fixture_dir <- tempfile("bambu-cli-dir-")
|
|
dir.create(fixture_dir)
|
|
bam_dir <- file.path(fixture_dir, "bams")
|
|
dir.create(bam_dir)
|
|
|
|
reference <- workflow_glue_r_fixture("bambu", "reference.fa")
|
|
annotation <- workflow_glue_r_fixture("bambu", "annotation.gtf")
|
|
reads <- workflow_glue_r_fixture("bambu", "reads.fastq")
|
|
expect_bam_fixture_built(reference, reads, bam_dir, alias = "sampleA")
|
|
expect_bam_fixture_built(reference, reads, bam_dir, alias = "sampleB")
|
|
sample_sheet <- file.path(fixture_dir, "sample_sheet.csv")
|
|
writeLines(
|
|
paste(
|
|
"alias",
|
|
"sampleB",
|
|
"sampleA",
|
|
sep = "\n"
|
|
),
|
|
sample_sheet
|
|
)
|
|
out_dir <- file.path(fixture_dir, "out")
|
|
|
|
result <- run_rscript(
|
|
"supeRglue",
|
|
c(
|
|
"bambu",
|
|
"--bams", paste(
|
|
c(
|
|
file.path(bam_dir, "sampleA.aligned.sorted.bam"),
|
|
file.path(bam_dir, "sampleB.aligned.sorted.bam")
|
|
),
|
|
collapse = ","
|
|
),
|
|
"--aliases", "sampleA,sampleB",
|
|
"--sample_sheet", sample_sheet,
|
|
"--annotation", annotation,
|
|
"--genome", reference,
|
|
"--transcriptome_mode", "fixed_annotation",
|
|
"--threads", "1",
|
|
"--out_dir", out_dir
|
|
)
|
|
)
|
|
|
|
testthat::expect_equal(
|
|
result$status,
|
|
0L,
|
|
info = paste(result$output, collapse = "\n")
|
|
)
|
|
|
|
samples <- utils::read.csv(
|
|
file.path(out_dir, "samples.csv"),
|
|
check.names = FALSE,
|
|
stringsAsFactors = FALSE
|
|
)
|
|
tx_counts <- utils::read.delim(
|
|
file.path(out_dir, "transcript_counts.tsv"),
|
|
check.names = FALSE,
|
|
stringsAsFactors = FALSE
|
|
)
|
|
|
|
testthat::expect_equal(samples$alias, c("sampleA", "sampleB"))
|
|
testthat::expect_true(all(c("sampleA", "sampleB") %in% names(tx_counts)))
|
|
testthat::expect_gt(nrow(tx_counts), 0)
|
|
})
|
|
|
|
# Ensembl GTF annotations include version numbers in IDs (e.g., ENST000001.7).
|
|
# Verify bambu doesn't strip versions during prepareAnnotations().
|
|
testthat::test_that("Ensembl versioned identifiers preserved", {
|
|
fixture_dir <- tempfile("annotation-ensembl-")
|
|
dir.create(fixture_dir)
|
|
|
|
gtf <- file.path(fixture_dir, "ensembl_versions.gtf")
|
|
writeLines(
|
|
c(
|
|
paste(
|
|
"chr1", "Ensembl", "transcript", "1", "100", ".", "+", ".",
|
|
'gene_id "ENSG000001.16"; transcript_id "ENST000001.7"; gene_name "GENEA";',
|
|
sep = "\t"
|
|
),
|
|
paste(
|
|
"chr1", "Ensembl", "exon", "1", "100", ".", "+", ".",
|
|
'gene_id "ENSG000001.16"; transcript_id "ENST000001.7"; exon_number "1";',
|
|
sep = "\t"
|
|
)
|
|
),
|
|
gtf
|
|
)
|
|
|
|
prepared <- bambu::prepareAnnotations(gtf)
|
|
testthat::expect_s4_class(prepared, "GRangesList")
|
|
testthat::expect_true("ENST000001.7" %in% names(prepared))
|
|
})
|