#'
#' @aliases proceedToCoex
#'
#' @details `proceedToCoex()` takes a newly created `COTAN` object (or the
#' result of a call to `dropGenesCells()`) and runs [calculateCoex()]
#'
#' @param objCOTAN a newly created `COTAN` object
#' @param calcCoex a Boolean to determine whether to calculate the genes' `COEX`
#' or stop just before at the [estimateDispersionBisection()] step
#' @param optimizeForSpeed Boolean; when `TRUE` `COTAN` tries to use the `torch`
#' library to run the matrix calculations. Otherwise, or when the library is
#' not available will run the slower legacy code
#' @param deviceStr On the `torch` library enforces which device to use to run
#' the calculations. Possible values are `"cpu"` to us the system *CPU*,
#' `"cuda"` to use the system *GPUs* or something like `"cuda:0"` to restrict
#' to a specific device
#' @param cores number of cores to use. Default is 1.
#' @param saveObj Boolean flag; when `TRUE` saves intermediate analyses and
#' plots to file
#' @param outDir an existing directory for the analysis output.
#'
#' @returns `proceedToCoex()` returns the updated `COTAN` object with genes'
#' `COEX` calculated. If asked to, it will also store the object, along all
#' relevant clean-plots, in the output directory.
#'
#' @export
#'
#' @importFrom utils write.csv
#'
#' @importFrom stats time
#'
#' @importFrom grDevices pdf
#' @importFrom grDevices dev.off
#' @importFrom grDevices colors
#'
#' @importFrom ggplot2 ggplot
#' @importFrom ggplot2 annotate
#'
#' @examples
#' #
#' # In case one needs to run more steps to clean the datatset
#' # the following might apply
#' if (FALSE) {
#' objCOTAN <- initializeMetaDataset(objCOTAN,
#' GEO = "test",
#' sequencingMethod = "artificial",
#' sampleCondition = "test dataset")
#' #
#' # doing all the cleaning...
#' #
#' # in case the genes' `COEX` is not needed it can be skipped
#' # (e.g. when calling [cellsUniformClustering()])
#' objCOTAN <- proceedToCoex(objCOTAN, calcCoex = FALSE,
#' cores = 6L, optimizeForSpeed = TRUE,
#' deviceStr = "cuda", saveObj = FALSE)
#' }
#'
#' @rdname COTANObjectCreation
#'
setMethod(
"proceedToCoex",
"COTAN",
function(objCOTAN, calcCoex = TRUE, optimizeForSpeed = TRUE,
deviceStr = "cuda", cores = 1L, saveObj = TRUE, outDir = ".") {
startTimeAll <- Sys.time()
logThis("Cotan analysis functions started", logLevel = 1L)
objCOTAN <- clean(objCOTAN)
if (isTRUE(saveObj)) {
if (!dir.exists(outDir)) {
dir.create(file.path(outDir))
}
cond <- getMetadataElement(objCOTAN, datasetTags()[["cond"]])
outDirCond <- file.path(outDir, cond)
if (!dir.exists(outDirCond)) {
dir.create(outDirCond)
}
outDirCleaning <- file.path(outDirCond, "cleaning")
if (!dir.exists(outDirCleaning)) {
dir.create(outDirCleaning)
}
plots <- cleanPlots(objCOTAN)
{
numIter <- 1L
pdf(file.path(outDirCleaning,
paste0(cond, "_", numIter,
"_plots_without_cleaning.pdf")))
plot(plots[["pcaCells"]])
plot(plots[["genes"]])
dev.off()
}
{
pdf(file.path(outDirCleaning,
paste0(cond, "_plots_PCA_efficiency_colored.pdf")))
plot(plots[["UDE"]])
dev.off()
}
{
pdf(file.path(outDirCleaning,
paste0(cond, "_plots_efficiency.pdf")))
plot(plots[["nu"]] +
annotate(geom = "text", x = 50L, y = 0.25,
label = "nothing to remove ", color = "darkred"))
dev.off()
}
rm(plots)
gc()
}
analysisTime <- Sys.time()
objCOTAN <- estimateDispersionBisection(objCOTAN, cores = cores)
gc()
if (isTRUE(calcCoex)) {
genesCoexTime <- Sys.time()
analysisTime <- difftime(genesCoexTime, analysisTime, units = "secs")
logThis(paste("Only analysis elapsed time:", analysisTime), logLevel = 3L)
logThis("Cotan genes' coex estimation started", logLevel = 1L)
objCOTAN <- calculateCoex(objCOTAN, actOnCells = FALSE,
optimizeForSpeed = optimizeForSpeed,
deviceStr = deviceStr)
gc()
endTime <- Sys.time()
genesCoexTime <- difftime(endTime, genesCoexTime, units = "secs")
logThis(paste("Only genes' coex elapsed time:", genesCoexTime),
logLevel = 3L)
} else {
logThis("Cotan genes' coex estimation not requested", logLevel = 2L)
genesCoexTime <- 0.0
gc()
endTime <- Sys.time()
}
allTime <- difftime(endTime, startTimeAll, units = "secs")
logThis(paste("Total elapsed time:", allTime), logLevel = 3L)
if (saveObj) {
utils::write.csv(data.frame("type" = c("tot_time",
"analysis_time",
"genes_coex_time"),
"times" = c(as.numeric(allTime),
as.numeric(analysisTime),
as.numeric(genesCoexTime)),
"n.cells" = getNumCells(objCOTAN),
"n.genes" = getNumGenes(objCOTAN)),
file = file.path(outDir, paste0(cond, "_times.csv")))
logThis(paste("Saving elaborated data locally at:",
file.path(outDir, paste0(cond, ".cotan.RDS"))),
logLevel = 1L)
saveRDS(objCOTAN, file = file.path(outDir, paste0(cond, ".cotan.RDS")))
}
return(objCOTAN)
}
)
#' @details `automaticCOTANObjectCreation()` takes a raw dataset, creates and
#' initializes a `COTAN` object and runs [proceedToCoex()]
#'
#' @param raw a matrix or dataframe with the raw counts
#' @param GEO a code reporting the GEO identification or other specific dataset
#' code
#' @param sequencingMethod a string reporting the method used for the sequencing
#' @param sampleCondition a string reporting the specific sample condition or
#' time point.
#' @param calcCoex a Boolean to determine whether to calculate the genes' `COEX`
#' or stop just before at the [estimateDispersionBisection()] step
#' @param optimizeForSpeed Boolean; when `TRUE` `COTAN` tries to use the `torch`
#' library to run the matrix calculations. Otherwise, or when the library is
#' not available will run the slower legacy code
#' @param deviceStr On the `torch` library enforces which device to use to run
#' the calculations. Possible values are `"cpu"` to us the system *CPU*,
#' `"cuda"` to use the system *GPUs* or something like `"cuda:0"` to restrict
#' to a specific device
#' @param cores number of cores to use. Default is 1.
#' @param saveObj Boolean flag; when `TRUE` saves intermediate analyses and
#' plots to file
#' @param outDir an existing directory for the analysis output.
#'
#' @returns `automaticCOTANObjectCreation()` returns the new `COTAN` object with
#' genes' `COEX` calculated. When asked, it will also store the object, along
#' all relevant clean-plots, in the output directory.
#'
#' @export
#'
#' @examples
#'
#' ## Otherwise it is possible to run all at once.
#' objCOTAN <- automaticCOTANObjectCreation(
#' raw = test.dataset,
#' GEO = "code",
#' sequencingMethod = "10X",
#' sampleCondition = "mouse_dataset",
#' calcCoex = TRUE,
#' saveObj = FALSE,
#' outDir = tempdir(),
#' cores = 6L)
#'
#' @rdname COTANObjectCreation
automaticCOTANObjectCreation <-
function(raw, GEO, sequencingMethod, sampleCondition,
calcCoex = TRUE, optimizeForSpeed = TRUE, deviceStr = "cuda",
cores = 1L, saveObj = TRUE, outDir = ".") {
objCOTAN <- COTAN(raw = raw)
objCOTAN <- initializeMetaDataset(objCOTAN, GEO = GEO,
sequencingMethod = sequencingMethod,
sampleCondition = sampleCondition)
logThis(paste0("Condition ", sampleCondition), logLevel = 2L)
logThis(paste("n cells", getNumCells(objCOTAN)), logLevel = 2L)
return(proceedToCoex(objCOTAN, calcCoex = calcCoex,
optimizeForSpeed = optimizeForSpeed,
deviceStr = deviceStr, cores = cores,
saveObj = saveObj, outDir = outDir))
}
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