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download and parsing of multimir for cormit has been updated
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,137 @@ | ||
| #!/usr/bin/env Rscript | ||
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| library(dplyr) | ||
| library(stringr) | ||
| library(data.table) | ||
| library(optparse) | ||
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| parse_validated_db <- function(multimir_summary, org_mirna_targets){ | ||
| for (db in c("mirecords", "mirtarbase", "tarbase")) { | ||
| database_pairs <- as.data.table(org_mirna_targets[org_mirna_targets$database == db, c("target_ensembl", "mature_mirna_acc")]) | ||
| database_pairs <- unique(database_pairs) | ||
| database_pairs <- paste0(database_pairs$target_ensembl, "_AND_", database_pairs$mature_mirna_acc) | ||
| db_pairs <- multimir_summary$pairs %in% database_pairs | ||
| multimir_summary[[db]] <- db_pairs | ||
| } | ||
| return(multimir_summary) | ||
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| } | ||
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| parse_predicted_db <- function(multimir_summary, org_mirna_targets, scale){ | ||
| prediction_databases <- c("diana_microt", "elmmo", "microcosm", "miranda","mirdb", "pictar", "pita", "targetscan") | ||
| prediction_databases <- prediction_databases[prediction_databases %in% unique(org_mirna_targets$database)] | ||
| org_mirna_targets_split <- split(org_mirna_targets, org_mirna_targets$database) | ||
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| parsed_db_pairs <- lapply(prediction_databases, function(db){ | ||
| db_info <- org_mirna_targets_split[[db]][, c("target_ensembl", "mature_mirna_acc", "score")] | ||
| db_info$score <- as.numeric(db_info$score) | ||
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| if (scale == "db_quantile"){ | ||
| db_info <- as.data.frame(scale_quantile(db_info = db_info, database = db)) | ||
| } else if (scale == "pairs_quantile"){ | ||
| db_info <- scale_r_score(db_info = db_info, database = db) | ||
| } else { | ||
| db_info$raw_score <- db_info$score | ||
| } | ||
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| database_pairs <- paste0(db_info$target_ensembl, "_AND_", db_info$mature_mirna_acc) | ||
| parsed_multimir_summary <- db_info[match(multimir_summary$pairs, database_pairs), c("raw_score","score")] | ||
| return(parsed_multimir_summary) | ||
| }) | ||
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| names(parsed_db_pairs) <- prediction_databases | ||
| for (database in names(parsed_db_pairs)) { | ||
| multimir_summary[, database] <- parsed_db_pairs[[database]]$score | ||
| multimir_summary[, paste0("raw_",database)] <- parsed_db_pairs[[database]]$raw_score | ||
| } | ||
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| return(multimir_summary) | ||
| } | ||
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| scale_quantile <- function(db_info, database){ | ||
| db_info <- as.data.frame(db_info) | ||
| db_info$raw_score <- db_info$score | ||
| if (database %in% c("targetscan", "pita","miranda")){ | ||
| db_info$score <- -1 * db_info$score | ||
| } | ||
| db_info$score <- rank(db_info$score) / nrow(db_info) | ||
| return(db_info) | ||
| } | ||
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| scale_r_score <- function(db_info, database){ | ||
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| mirnas_pairs <- split(db_info, db_info$mature_mirna_acc) | ||
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| parsed_mirna_pairs <- lapply(mirnas_pairs, function(mirna_pairs){ | ||
| mirna_pairs$raw_score <- mirna_pairs$score | ||
| if (database %in% c("targetscan", "pita", "miranda")){ | ||
| mirna_pairs$score <- mirna_pairs$score * -1 | ||
| } | ||
| mirna_pairs$score <- rank(mirna_pairs$score) / nrow(mirna_pairs) | ||
| return(mirna_pairs) | ||
| }) | ||
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| parsed_mirna_pairs <- data.table::rbindlist(parsed_mirna_pairs) | ||
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| return(as.data.frame(parsed_mirna_pairs)) | ||
| } | ||
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| option_list <- list( | ||
| optparse::make_option(c("-i", "--input"), type= "character", default = ".", | ||
| help = "Set input folder"), | ||
| optparse::make_option(c("-r", "--report_mode"), type= "logical", action = "store_true", default = FALSE, | ||
| help = "Activate report mode, load input/parsed_[org].RData and print report."), | ||
| optparse::make_option(c("--organism"), type = "character", default = NULL, | ||
| help = "Set the model organisms available on multimiR (hsa, mmu or rno)"), | ||
| optparse::make_option(c("-s", "--scale"), type = "character", default = "raw_score", | ||
| help = "Scaling method can be set 'raw_score', 'pairs_quantile' (specific miRNA quantile) or 'db_quantile' (whole database quantiles). Default=%default"), | ||
| optparse::make_option(c("-o","--output"), type = "character", default = ".", | ||
| help = "Set the output path. Parsed multimir will be saved in parsed_[organism]_[scale].RData") | ||
| ) | ||
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| opt <- optparse::parse_args(optparse::OptionParser(option_list = option_list)) | ||
| load(file.path(opt$input, paste0(opt$organism, ".RData"))) | ||
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| message("Filtering starts") | ||
| org_mirna_targets <- org_mirna_targets[org_mirna_targets$target_ensembl != "" & | ||
| org_mirna_targets$mature_mirna_acc != "" & | ||
| !is.na(org_mirna_targets$database),] | ||
| databases_names <- unique(org_mirna_targets$database) | ||
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| all_pairs <- as.data.table(org_mirna_targets[, c("target_ensembl", "mature_mirna_acc")]) | ||
| all_pairs <- unique(all_pairs) | ||
| unique_pairs <- paste0(all_pairs$target_ensembl, "_AND_", all_pairs$mature_mirna_acc) | ||
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| message("Unique starts") | ||
| multimir_summary <- data.table(pairs = unique_pairs) | ||
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| message("Parsing validated databases") | ||
| multimir_summary <- parse_validated_db(multimir_summary = multimir_summary, | ||
| org_mirna_targets = org_mirna_targets) | ||
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| message("Parsing prediction databases") | ||
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| multimir_summary <- parse_predicted_db(multimir_summary= multimir_summary, org_mirna_targets = org_mirna_targets, scale = opt$scale) | ||
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| new_columns <- str_split_fixed(multimir_summary$pairs, "_AND_", n = 2) | ||
| multimir_summary$target_ensembl <- new_columns[,1] | ||
| multimir_summary$mature_mirna_acc <- new_columns[,2] | ||
| multimir_summary$pairs <- NULL | ||
| multimir_summary <- as.data.frame(multimir_summary) | ||
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| # } | ||
| if (opt$scale != "raw_score"){ | ||
| message("Rendering report") | ||
| rmarkdown::render(file.path(".", 'multiMiR_parsing.Rmd'), | ||
| output_file = file.path(opt$output, paste0("multiMiR_",opt$organism,"_", opt$scale, "_test_report.html")), intermediates_dir = opt$output) | ||
| } else { | ||
| message("Scores will not be scaled") | ||
| } | ||
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| message("Saving data") | ||
| multimir_summary[,grepl("raw", colnames(multimir_summary))] <- NULL | ||
| save(multimir_summary, file = file.path(opt$output, paste0("parsed_", opt$scale, "_", opt$organism, ".RData"))) |