library(MSstatsConvert)
library(MSstats)
library(stringr)
library(parallel)
library(jsonlite)
source("calculateMetrics.R")
config <- fromJSON("scriptController.json", simplifyVector = FALSE)
dataset_config <- config$datasets[["DDA-Dowell2021-HEqe408_LFQ"]]
dataset_config <- as.list(dataset_config)
cat("Processing Dataset:", dataset_config$name, "\n")
cat("Dataset File Path:", dataset_config$file, "\n")
start_time <- Sys.time()
fragpipe_raw <- data.table::fread(dataset_config$file)
head(fragpipe_raw)
msstats_format = MSstatsConvert::FragPipetoMSstatsFormat(fragpipe_raw, use_log_file = FALSE)
data_process_tasks <- list(
list(
label = "Data process with Normalized Data",
result = function() dataProcess(msstats_format, featureSubset = "topN", n_top_feature = 20)
),
list(
label = "Data process with Normalization and MBImpute False",
result = function() dataProcess(msstats_format, featureSubset = "topN", n_top_feature = 20, MBimpute = FALSE)
),
list(
label = "Data process without Normalization",
result = function() dataProcess(msstats_format, featureSubset = "topN", normalization = "FALSE", n_top_feature = 20)
),
list(
label = "Data process without Normalization with MBImpute False",
result = function() dataProcess(msstats_format, featureSubset = "topN", normalization = "FALSE", n_top_feature = 20, MBimpute = FALSE)
)
)
start_time <- Sys.time()
num_cores <- detectCores() - 1
summarized_results <- mclapply(data_process_tasks, function(task) {
list(label = task$label, summarized = task$result())
}, mc.cores = num_cores)
results_list <- mclapply(summarized_results, function(res) {
calculateResult(res$summarized, res$label, dataset_config$samples)
}, mc.cores = num_cores)
final_results <- do.call(rbind, results_list)
end_time <- Sys.time()
total_time <- end_time - start_time
print(final_results)
print(paste("Total Execution Time:", total_time))
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