Feature/fix dims peak finding functions

DIMS/CollectFilled.R

@@ -27,7 +27,7 @@ for (scanmode in scanmodes) {
   repl_pattern <- get(load(pattern_file))
   # calculate Z-scores
   if (z_score == 1) {
-    outlist_stats <- calculate_zscores(outlist_total, adducts = FALSE)
+    outlist_stats <- calculate_zscores(outlist_total)
     nr_removed_samples <- length(which(repl_pattern[] == "character(0)"))
     order_index_int <- order(colnames(outlist_stats)[8:(length(repl_pattern) - nr_removed_samples + 7)])
     outlist_stats_more <- cbind(

DIMS/EvaluateTics.R

@@ -13,6 +13,10 @@ highest_mz_file <- cmd_args[5]
 highest_mz <- get(load(highest_mz_file))
 trim_params_filepath <- cmd_args[6]
 thresh2remove <- 1000000000
+preprocessing_scripts_dir <- cmd_args[7]
+
+# load functions
+source(paste0(preprocessing_scripts_dir, "evaluate_tics_functions.R"))
 
 # load init_file: contains repl_pattern
 load(init_file)
@@ -41,35 +45,11 @@ remove_neg <- remove_tech_reps$neg
 repl_pattern_filtered <- remove_from_repl_pattern(remove_neg, repl_pattern, nr_replicates)
 save(repl_pattern_filtered, file = "negative_repl_pattern.RData")
 
-# write output for QC info on missed infusions
-if (is.null(remove_neg)) {
-  remove_neg <- "none"
-}
-write.table(
-  remove_neg, 
-  file = "miss_infusions_negative.txt", 
-  row.names = FALSE, 
-  col.names = FALSE, 
-  sep = "\t"
-)
-
 # positive scan mode
 remove_pos <- remove_tech_reps$pos
 repl_pattern_filtered <- remove_from_repl_pattern(remove_pos, repl_pattern, nr_replicates)
 save(repl_pattern_filtered, file = "positive_repl_pattern.RData")
 
-# write output for QC info on missed infusions
-if (is.null(remove_pos)) {
-  remove_pos <- "none"
-}
-write.table(
-  remove_pos, 
-  file = "miss_infusions_positive.txt", 
-  row.names = FALSE, 
-  col.names = FALSE, 
-  sep = "\t"
-)
-
 # get an overview of suitable technical replicates for both scan modes
 allsamples_techreps_neg <- get_overview_tech_reps(repl_pattern_filtered, "negative")
 allsamples_techreps_pos <- get_overview_tech_reps(repl_pattern_filtered, "positive")
@@ -81,6 +61,7 @@ write.table(allsamples_techreps_both_scanmodes,
             sep = ","
 )
 
+
 ## generate TIC plots
 # get all txt files
 tic_files <- list.files("./", full.names = TRUE, pattern = "*TIC.txt")
@@ -158,3 +139,4 @@ tic_plot_pdf <- marrangeGrob(
 ggsave(filename = paste0(run_name, "_TICplots.pdf"),
        tic_plot_pdf, width = 21, height = 29.7, units = "cm")
 
+

DIMS/EvaluateTics.nf

@@ -26,7 +26,8 @@ process EvaluateTics {
                                                              $analysis_id \
                                                              $params.matrix \
                                                              $highest_mz_file \
-                                                             $trim_params_file
+                                                             $trim_params_file \
+                                                             $params.preprocessing_scripts_dir
         """
 }
 

DIMS/preprocessing/peak_finding_functions.R

@@ -83,7 +83,7 @@ search_regions_of_interest <- function(ints_fullrange) {
 
   # sort on first index
   if (nrow(regions_of_interest_final) > 1){
-    regions_of_interest_sorted <- regions_of_interest_final %>% dplyr::arrange(from)
+    regions_of_interest_sorted <- regions_of_interest_final %>% as.data.frame %>% dplyr::arrange(from)
   } else {
     regions_of_interest_sorted <- regions_of_interest_final
   }