Nothing
context("msSummarize() related functions")
# ### Read in data files
# pathclinical <- system.file("data-raw", "Clinical.csv",
# package = "MSPrep")
# pathquant <- system.file("extdata", "Quantification.csv", package = "MSPrep")
# pathlink <- system.file("data-raw", "SubjectLinks.csv", package = "MSPrep")
# path_olddata <- system.file("extdata", "old_object.Rda", package = "MSPrep")
# data(msquant_subject1)
# quant <- msquant_subject1
# load(path_olddata)
#
# Generate tidy dataset from wide quant data
# tidy_data <- ms_tidy(quant, mz = "mz", rt = "rt",
# col_extra_txt = "Neutral_Operator_Dif_Pos_",
# separator = "_",
# col_names = c("spike", "batch", "replicate",
# "subject_id"))
# prepped_data <- tidy_data %>% ms_summarize(mz = "mz",
# rt = "rt",
# replicate = "replicate",
# batch = "batch",
# groupingvars = "spike")
data(msquant)
preppedData <- msSummarize(msquant,
returnSummaryDetails = TRUE,
compVars = c("mz", "rt"),
sampleVars = c("spike", "batch", "replicate",
"subject_id"),
colExtraText = "Neutral_Operator_Dif_Pos_",
separator = "_",
missingValue = 1)
summaryDetails <- preppedData$summaryDetails
preppedData <- preppedData$data
test_that(".replaceMissing()", {
# numeric to NA
testVec <- c(4, 6, 1, 9, 5, 8, 3, 7, 10, 1)
replacedVec <- MSPrep:::.replaceMissing(testVec, missingValue = 1,
setMissing = NA)
expect_equal(sum(is.na(replacedVec)), 2)
expect_equal(replacedVec[c(3, 10)], c(NA_real_, NA_real_))
# numeric to numeric
replacedVec <- MSPrep:::.replaceMissing(testVec, missingValue = 1,
setMissing = 0)
expect_equal(sum(replacedVec == 0), 2)
expect_equal(replacedVec[c(3, 10)], c(0, 0))
# character to NA
testVec <- as.character(testVec)
replacedVec <- MSPrep:::.replaceMissing(testVec,
missingVal = as.character(1),
setMissing = NA)
expect_equal(sum(is.na(replacedVec)), 2)
expect_equal(replacedVec[c(3, 10)], c(NA_character_, NA_character_))
})
test_that(".selectSummaryMeasure", {
nReplicates <- 3
cvMax <- 0.50
minPropPresent <- 1/3
dat <- expand.grid(nPresent = c(0, 1, 2, 3), cvAbundance = c(0.1, 0.5, 1.0))
results <- MSPrep:::.selectSummaryMeasure(dat$nPresent, dat$cvAbundance,
nReplicates, minPropPresent, cvMax)
expect_equal(results, c("none: proportion present <= minProportionPresent",
"none: proportion present <= minProportionPresent",
"mean", "mean",
"none: proportion present <= minProportionPresent",
"none: proportion present <= minProportionPresent",
"mean", "mean",
"none: proportion present <= minProportionPresent",
"none: proportion present <= minProportionPresent",
"none: cv > cvMax & 2 present",
"median"))
})
test_that("Summary measure count remains constant in test data", {
expect_equal(46, sum(summaryDetails$summaryMeasure == "median"))
expect_equal(23872, sum(summaryDetails$summaryMeasure == "mean"))
expect_equal(23638,
sum(summaryDetails$summaryMeasure ==
"none: proportion present <= minProportionPresent"))
expect_equal(36,
sum(summaryDetails$summaryMeasure ==
"none: cv > cvMax & 2 present"))
})
# test_that("New version of summarized dataset matches old version", {
#
# # Add old readdata() function that creates old_readdata_result
# # in inst/extdata/
# # folder
#
# # read in old dataset
# # load("R/sysdata.rda")
#
# sum_data <-
# prepped_data$data %>%
# dplyr::select(batch, spike, mz, rt, abundance_summary) %>%
# tidyr::unite(id, spike, batch, sep = "_") %>%
# tidyr::unite(metabolite, mz, rt, sep = "_")
#
# #new_sum_data <- sum_data %>%
# # tidyr::spread(key = id, value = abundance_summary)
#
# old_sum <- old_readdata_result$sum_data1
# old_sum <- old_sum %>% as.data.frame
# old_sum <- old_sum %>% tibble::rownames_to_column(var = "id")
# old_sum <- old_sum %>% tidyr::gather(key = metabolite,
# value = abundance_summary, -id)
# old_sum <- old_sum %>% tibble::as_tibble(.)
# old_sum <- old_sum %>% dplyr::select(id, metabolite, abundance_summary)
# old_sum <- old_sum %>% tidyr::separate(metabolite, into = c("mz", "rt"),
# sep = "_")
# old_sum <- old_sum %>% dplyr::mutate(mz = as.numeric(mz))
# # old_sum <- mutate(rt = as.numeric(rt))
# old_sum <- old_sum %>% dplyr::arrange(mz)
# old_sum <- old_sum %>% tidyr::unite(metabolite, mz, rt, sep = "_")
#
# new <- sum_data %>% tidyr::separate(metabolite, into = c("mz", "rt"),
# sep = "_") %>%
# dplyr::arrange(id, mz, rt)
# old <- old_sum %>% tidyr::separate(metabolite, into = c("mz", "rt"),
# sep = "_") %>%
# dplyr::arrange(id, mz, rt)
# diffrows <- !(new == old)[, 4]
# newdiffs <- new[diffrows, ] %>% dplyr::rename(new_summary =
# abundance_summary)
# olddiffs <- old[diffrows, ] %>% dplyr::rename(old_summary =
# abundance_summary)
#
# expect_true(nrow(dplyr::anti_join(old_sum, sum_data)) == 0)
#
# #comparing <-
# # .data %>% mutate(mz = as.character(mz), rt = as.character(rt)) %>%
# # unite(id, spike, subject_id) %>%
# # right_join(., newdiffs) %>%
# # left_join(., olddiffs)
#
# expect_true(identical(new, old))
#
# })
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