#################### Reload previously saved and normalized counts ####################
## Define the path of the memory image file
featureType <- project.parameters$global$feature
studyCases.mem.image <- file.path(
project.parameters$global$dir$memoryImages,
paste0(
paste(collapse = "-", selectedRecountIDs),
"_", featureType,
"_loaded_studyCase.Rdata"))
## Reload previously stored memory image
message.with.time("Reloading study cases from previously stored memory image",
"\n\t", studyCases.mem.image)
system.time(load(studyCases.mem.image))
## Indicate that this script has finished running
message.with.time("Finished running 02b_reload_counts.R")
## Print out some statistics about the data sizes
message("Data set characteristics")
## Extract parameters for the current study case
studyCase <- studyCases[[recountID]]
# View(studyCase)
message("Computing filtering impact for study case ", recountID)
message("Recount ID: ", studyCase$rawData$countsPerSample$parameters$recountID)
message("Name: ", studyCase$rawData$countsPerSample$parameters$short_label)
datasetParameters <- data.frame(
beforeFiltering = c(
nbClasses = studyCase$rawData$countsPerSample$nbClasses,
nbSamples = studyCase$rawData$countsPerSample$nbSamples,
nbGenes = studyCase$rawData$countsPerSample$nbGenes
),
afterFiltering = c(
nbClasses = studyCase$datasetsForTest$filtered$nbClasses,
nbSamples = studyCase$datasetsForTest$filtered$nbSamples,
nbGenes = studyCase$datasetsForTest$filtered$nbGenes
)
)
discarded_feature = c(
nbGenes = studyCase$rawData$countsPerSample$nbGenes,
kept_after_NZVF = length(studyCase$datasetsForTest$filtered$keptGenes),
discarded_by_NZVF = studyCase$rawData$countsPerSample$nbGenes - length(studyCase$datasetsForTest$filtered$keptGenes)
)
datasetParameters$discarded <- datasetParameters$beforeFiltering - datasetParameters$afterFiltering
print(datasetParameters)
print(discarded_feature )
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