curatedPCaData: Curated Prostate Cancer Data

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#
# Native 'R CMD check' tests run on the 'curatedPCaData'-package
# Any exceptions will count as a failure for 'R CMD check' run (notably, does 
# not require 'RUnit' or 'testthat' packages for testing)
#
###

##
# Testing of getPCa main functionality
##

# Test retrieval of TCGA with all assays
# Get default fetching of a MAE object based on short id
methods::is(curatedPCaData::getPCa("tcga"), "MultiAssayExperiment")

# Test retrieval of Taylor with a pre-specified subset of assays
# Get fetching of an assay subset
methods::is(curatedPCaData::getPCa("taylor", assays = c("gex.rma", "cibersort", 
    "scores")), "MultiAssayExperiment")

# Test a data fetch that should result in an error
# Test that an error is produced correctly for a study that does not exist
methods::is(try({curatedPCaData::getPCa("studyname_misspelled", assays = 
    c("foo", "bar"))}, silent=TRUE), "try-error")

# Test fetching of an assay that does not exist
# Test that an error is produced correctly for assays that do not exist
methods::is(try({curatedPCaData::getPCa("tcga", assays = "typo")}, 
    silent=TRUE), "try-error")

# Test sample subtype subsetting during getPCa
# Get only primary samples from TCGA
all(curatedPCaData::getPCa("tcga", sampletypes = "primary")$sample_type == 
    "primary")
# Test omitting metastatic samples from Chandran et al.
all(curatedPCaData::getPCa("chandran", sampletypes = c("primary", "normal")
    )$sample_type %in% c("primary", "normal"))

##
# Testing of supporting summary functions etc
##

# Test fetching of study short ids and that the 19 studies originally available 
# in Laajala et al. 2013 are retrieved correctly
# Tested function: curatedPCaData::getPCaStudies
studies <- curatedPCaData::getPCaStudies()
all(c("abida", "baca", "barbieri", "barwick", "chandran", "friedrich", 
    "hieronymus", "icgcca", "igc", "kim", "kunderfranco", "ren", "sun", 
    "taylor", "tcga", "true", "wallace", "wang", "weiner") %in% studies)

# Fetch MAE objects for further use
maes <- lapply(studies, FUN=\(id) { curatedPCaData::getPCa(id) })
names(maes) <- studies

# getPCaSummaryTable should summarize into a character matrix key instances and 
# percentages for certain values for a given colData metadata variable
# Tested function: curatedPCaData::getPCaSummaryTable
inherits(curatedPCaData::getPCaSummaryTable(maes, var.name = "grade_group", 
    vals=c("<=6", "3+4", "4+3", "7", ">=8")), "matrix")

# getPCaSummaryTable should summarize into a character matrix event counts and 
# follow-up times for a Surv-like data
# Tested function: curatedPCaData::getPCaSummarySurv
inherits(curatedPCaData::getPCaSummarySurv(maes, event.name = 
    "disease_specific_recurrence_status", 
    time.name = "days_to_disease_specific_recurrence"), "matrix")

# getPCaSummarySamples should return a list of length 2; first element 
# containing unique assay names and N counts in each study, and second element 
# a matrix with GEX/CNA/MUT combinations for overlap
# Tested function: curatedPCaData::getPCaSummarySamples
inherits(curatedPCaData::getPCaSummarySamples(maes), "list")
length(curatedPCaData::getPCaSummarySamples(maes)) == 2

# getPCaSummaryStudies should create a verbose character matrix depicting key 
# characteristics for each study, such as sample counts, platforms, and special 
# notes to be aware of
# Tested function: curatedPCaData::getPCaSummaryStudies, 
# curatedPCaData::getPCaStudies
inherits(curatedPCaData::getPCaSummaryStudies(maes), "matrix")