mstools: OpenSWATH Mass Spectrometry Data Exploration

Documented in XICMasterPlotFcn_

#// **********************************************************************************************
#//                         XICMasterPlotFcn.R
#// **********************************************************************************************
#//
#// 
#// **********************************************************************************************
#// @Maintainer: Justin Sing
#// @Author: Justin Sing

#' @export
#' @title Master Plotting function for plotting XIC's
#' @description This function can be used to draw XIC's by calling getXIC 
#' 
#' @param dup_peps A character vector of a peptide sequence(s). 
#' @param uni_mod A character vector of a modified peptide sequence. (Default: NULL) If using this argument, dup_peps must be a single peptide
#' @param sqMass_files A list of character vectors. Full paths to chromatogram file(s).
#' @param in_lib A character vector. Full path to a pqp assay library.
#' @param in_osw A character vector. Full path to an osw results file.
#' @param .osw_df (data.table) A data.table object that contains the osw results peak group scores data
#' @param .lib_df (data.table) A data.table object that contains lib information
#' @param SCORE_IPF Do you want to extract IPF Score if present? (Default: FALSE. will use MS2 m-scores)
#' @param plotPrecursor A logical value. True will plot precursor chromatogram
#' @param plotIntersectingDetecting A logcail value. True will plot intersecting detecting transitions if comparing two peptidoforms.
#' @param plotUniqueDetecting A logical value. True will plot unique detecting transitions if comparing two peptidoforms.
#' @param plotIdentifying A logical value. True will plot identifying transitions.
#' @param plotIdentifying.Unique A logical value. True will plot unique identifying transitions.
#' @param plotIdentifying.Shared A logical value. True will plot shared identifying transitions. (Decaprecated)
#' @param plotIdentifying.Against A logical value. True will plot against identifying transitions. (Decaprecated)
#' @param smooth_chromatogram A list containing p (numeric) for the polynomial order for sgolay, and n (numeric) the bandwidth for sgolay. (Defualt: list(p=4, n=9)
#' @param doFacetZoom A logical valie. Should the plot be zoomed in. The default zooming operation is based on the max int divided by 4. (Default: FALSE)
#' @param FacetFcnCall A facet_zoom function with user defined parameters. i.e. FacetFcnCall = facet_zoom(xlim = c(7475, 7620), ylim = c(0, 4000) ). (Default: NULL)
#' @param doPlot A logical value. TRUE will perform steps to save the plot as a pdf.
#' @param Charge_State A numeric value. The target charge state. (Default: NULL)
#' @param N_sample A numeric value. The number of peptidoforms to sample, if more than one. (Default: 1)
#' @param idx_draw_these A vector of numeric values. The indices for the peptidoforms you wish to exaine.
#' @param store_plots_subdir A character vector. The location to store plots.
#' @param printPlot A logical value. TRUE will print plot in RStudio display.
#' @param use_top_trans_pep A logical value. TRUE will rank the transitions based on the posterior error probabilities.
#' @param transition_selection_list A list containing transitions to display for unique identifying. i.e. transition_selection_list <- list( y = c(3), b = c(8:10) )
#' @param show_n_transitions A numeric value. Show n number of transitions
#' @param show_transition_scores A logical value. If set to TRUE, will include TRANSITION PEPs as text tag when using interactive plotly.
#' @param annotate_best_pkgrp A logical value. Annotate Top Peak Group
#' @param show_all_pkgrprnk A logical value. Show all feature peak-group ranks. Usually 5. (Default: 5)
#' @param show_manual_annotation A dataframe with leftWidth and rightWidth retention time boundary values of a manually annotated peak. Will draw a transparent blue shaded rectangle indicating manual annotation. I.e data.frame(leftWidth=300, rightWidth=330)
#' @param show_legend A logical value. Display legend information for transition id, m/z and charge. (Default: TRUE)
#' @param mzPntrs A list object containing cached mzR objects.
#' 
#' @return A ggplot-grobs table of a XIC
#' 
#' @author Justin Sing \url{https://github.com/singjc}
#' @importFrom  tictoc tic toc
#' @importFrom crayon blue red underline magenta bold 
#' @importFrom parallel mclapply detectCores
#' @importFrom ggplot2 ggplot
#' @importFrom dplyr %>% filter select distinct arrange
#' @importFrom stringr str_replace_all
#' @importFrom MazamaCoreUtils logger.isInitialized logger.info logger.error logger.warn logger.trace
XICMasterPlotFcn_ <- function( dup_peps, 
                               uni_mod=NULL, 
                               sqMass_files,  
                               in_lib, 
                               in_osw, 
                               .osw_df=NULL,
                               .lib_df=NULL,
                               SCORE_IPF=FALSE,
                               plotPrecursor=T,
                               plotIntersectingDetecting=T,
                               plotUniqueDetecting=F,
                               plotIdentifying=F,
                               plotIdentifying.Unique=F,
                               plotIdentifying.Shared=F,
                               plotIdentifying.Against=F,
                               smooth_chromatogram=list(p = 4, n = 9), 
                               retention_time_limits = list(minRT=-Inf, maxRT=Inf),
                               doFacetZoom=F,
                               FacetFcnCall=NULL,
                               doPlot=T,
                               Charge_State=NULL,
                               N_sample=1,
                               idx_draw_these=NULL,
                               store_plots_subdir = '/Results/',
                               printPlot=F,
                               use_top_trans_pep=F,
                               transition_selection_list=NULL,
                               show_n_transitions=NULL,
                               show_transition_scores=FALSE,
                               annotate_best_pkgrp=TRUE,
                               show_all_pkgrprnk=T,
                               show_peak_info_tbl=F,
                               show_manual_annotation=NULL,
                               show_legend=T,
                               mzPntrs=NULL
                               ){
  
  # Get XICs for Modified Peptides  ---------------------------------------------------------------
  
  ## Check if logging has been initialized
  if( !MazamaCoreUtils::logger.isInitialized() ){
    log_setup()
  }
  
  tictoc::tic( paste('XIC plotting for ', length(dup_peps), ' peptides took: ', sep=' '))
  pep_counter = 0
  for ( pep in dup_peps){
    pep_counter = pep_counter + 1
    MazamaCoreUtils::logger.info( crayon::blue('#', pep, ' | (',pep_counter, ' of ', length(dup_peps), ')\n', sep='') )
   
    record_list <- parallel::mclapply( seq(1,length(sqMass_files),1), function(file_idx){
      in_sqMass <- sqMass_files[file_idx]
      run_name <- gsub('_osw_chrom[.]sqMass$|[.]chrom.mzML$|[.]chrom.sqMass$|_osw[.]chrom[.]sqMass', '', basename(in_sqMass))
      run <- gsub('_SW*|_SW_0|(*_-_SW[.]mzML[.]gz|[.]chrom[.]sqMass)', '', gsub('yanliu_I170114_\\d+_|chludwig_K150309_|lgillet_L\\d+_\\d+-Manchester_dirty_phospho_-_', '', run_name))
      
      MazamaCoreUtils::logger.info( crayon::blue('@ Run: ', run),'\n', sep='' )

      plot_chrom_error <- tryCatch({
        
        #####################################
        ## TIME LIMIT FOR SCRIPT EXECUTION ##
        #####################################
        ## Time limit in seconds
        time_limit <- 4000 
        
        setTimeLimit(cpu = time_limit, elapsed = time_limit, transient = TRUE)
        on.exit({
          setTimeLimit(cpu = Inf, elapsed = Inf, transient = FALSE)
        })
        
        MazamaCoreUtils::logger.info(paste('   ~ Getting peptide library data.. for ', pep, '\n', sep=''))
        if ( is.null(.lib_df) ){
        # Retrieve library data for specific peptide
          df_lib <- getPepLibData_( in_lib, peptide_id=pep )
        } else {
          df_lib <- .lib_df
          df_lib %>%
            dplyr::filter( UNMODIFIED_SEQUENCE %in% dup_peps) -> df_lib # instead of UNMODIFIED_SEQUENCE this was Sequence before...
          MazamaCoreUtils::logger.info( "A data.frame object containing library data was supplied" )
        }
        
        MazamaCoreUtils::logger.info(paste('   ~ Getting OpenSwath data.. for ', pep, '\n', sep=''))
        tryCatch( expr = {
          # Load OSW Merged df
          if ( is.null(.osw_df) ){
          osw_df <- mstools::getOSWData_( in_osw, run_name, precursor_id='', peptide_unmodified = pep, mod_residue_position='', peak_group_rank_filter=T, pep_list='', ipf_filter='', ms2_score=T, ipf_score=F )
          if ( dim(osw_df)[1]==0 ){ MazamaCoreUtils::logger.error(paste(crayon::red(pep, ' was not found as a peak_rank_group=1 in osw file!!!, skipping...\n'),sep='')); return(list()) }
          } else {
            osw_df <- .osw_df
            osw_df %>%
              dplyr::filter( Sequence %in% dup_peps) %>%
              dplyr::filter( grepl( paste0(".*", run_name, ".*"), filename) ) -> osw_df
            MazamaCoreUtils::logger.info( "A data.frame object containing peak group scoring data was supplied" )
          }
        }, error = function(e){
          MazamaCoreUtils::logger.error(sprintf("[mstools::XICMasterPlotFcn_] There was the following error that occured while extracting OpenSWATH data: %s", e$message))
        })
        
        
        # Get unique number of modifications
        uni_mod_precursor_id <- unique(paste( df_lib$MODIFIED_SEQUENCE, df_lib$PRECURSOR_ID, df_lib$PRECURSOR_CHARGE, sep='_'))
        
        # Get the unique desired modifications found in openswath/pyprophet results
        desired_uni_mods <- grep(paste(paste(osw_df$id_precursor, osw_df$Charge, sep='_'), collapse = '|'), uni_mod_precursor_id, value=T)
        
        # Get the charge states of the uni modifications found in OSW results
        current_uni_mod_charges <- as.numeric(gsub('.*_', '', desired_uni_mods))

        # Get a list of unique modifications
        #### @TODO: make this more versatile for cases with more than 2 isoforms
        if (is.null(uni_mod)){
          uni_mod <- gsub('_\\d+_\\d+$', '', desired_uni_mods[grep( paste('_\\d+_',mstools::Mode(as.numeric(gsub('.*_', '', desired_uni_mods))), sep=''), desired_uni_mods)])
          uni_mod <- sort(uni_mod)
          # uni_mod <- uni_mod[1:2]
          if(length(uni_mod)>2 | N_sample==1){
            MazamaCoreUtils::logger.info(paste(crayon::red('There are more than 2 Modification forms for', crayon::underline(pep),'\n Currently cannot handle more than 2 peptidoforms...\nPetidoforms:\n', paste(uni_mod,collapse='\n'),'\n\n', sep='')))
            
            MazamaCoreUtils::logger.info(paste('Will randomly sample 2 of the ', length(uni_mod), ' peptidoforms to process...\n'))
            n_mod_sites <- str_count( uni_mod, '\\(UniMod:21\\)|\\(Phospho\\)' ) + (str_count( uni_mod, '\\(UniMod:35\\)|\\(Oxidation\\)' )*3) + (str_count( uni_mod, '\\(UniMod:4\\)|\\(Carbamidomethyl\\)' )*6)
            n_mod_sites_common <- mstools::Mode(n_mod_sites)
            MazamaCoreUtils::logger.info( crayon::magenta$underline('There is(are) ', crayon::bold(length(n_mod_sites_common)), ' group(s) of isoforms...\n'))
            if ( !is.null(idx_draw_these) ){
              uni_isoform_group_list <- list()
              uni_isoform_group_list[[1]] <- uni_mod[idx_draw_these]
            } else {
              if ( length(uni_mod)==1 ){ n_sample=1 } else { n_sample=N_sample }
              # if ( length(unique(n_mod_sites_common))!=(length(uni_mod))/2 ){ cat(crayon::red('These are not isoforms of each other... Skipping... \n')); return(list()) }
              uni_isoform_group_list <- lapply(n_mod_sites_common, function(isoform_group){ sample(uni_mod[grepl(paste('^',isoform_group,'$',sep=''), n_mod_sites)], n_sample) })
              uni_isoform_group_list[[1]] <- sort(uni_isoform_group_list[[1]])
            }
            
          } else {
            n_mod_sites <- str_count( uni_mod, '\\(UniMod:21\\)|\\(Phospho\\)' ) + (stringr::str_count( uni_mod, '\\(UniMod:35\\)|\\(Oxidation\\)' )*3) + (stringr::str_count( uni_mod, '\\(UniMod:4\\)|\\(Carbamidomethyl\\)' )*6)
            n_mod_sites_common <- mstools::Mode(n_mod_sites)
            if ( length(uni_mod)==1 ){ mod=uni_mod; max_Int=0; return( drawNakedPeptide_(df_lib=df_lib, mod=mod, pep=pep, in_sqMass=in_sqMass, plotPrecursor=plotPrecursor, plotIntersectingDetecting=plotIntersectingDetecting,  plotIdentifying=plotIdentifying, plotUniqueDetecting=plotUniqueDetecting, plotIdentifying.Unique=plotIdentifying.Unique, plotIdentifying.Shared=plotIdentifying.Shared, plotIdentifying.Against=plotIdentifying.Against, intersecting_mz=NULL, uni_mod_list=NULL, max_Int=max_Int, in_osw=in_osw, smooth_chromatogram=smooth_chromatogram, doFacetZoom=doFacetZoom, top_trans_mod_list=NULL, show_all_pkgrprnk=show_all_pkgrprnk, FacetFcnCall=FacetFcnCall, show_legend = show_legend ) ) } 
            if ( length(uni_mod)==1 ){ n_sample=1 } else { n_sample=2 } # @TODO: Need to figure something out for this and the line above...
            if ( length(unique(n_mod_sites_common))!=(length(uni_mod))/2 ){ MazamaCoreUtils::logger.error(crayon::red('These are not isoforms of each other... Skipping... \n')); return(list()) }
            uni_isoform_group_list <- lapply(n_mod_sites_common, function(isoform_group){ sample(uni_mod[grepl(paste('^',isoform_group,'$',sep=''), n_mod_sites)], n_sample) })
            
          }
        }else {
          uni_isoform_group_list <- uni_mod
        }
        # modification_types_found <- unique(unlist(str_extract_all(uni_mod_list, '(UniMod:\\d+)')))
        len_unmod <- nchar( unique(df_lib$UNMODIFIED_SEQUENCE) )
        final_g_list <- list()
        for ( uni_isoform_group_list_idx in seq(1,length(uni_isoform_group_list),1) ){
          skipped_bool=FALSE
          uni_mod <- uni_isoform_group_list[[uni_isoform_group_list_idx]]
          uni_mod <- stringr::str_replace_all(uni_mod, "\t|\n", "")
          osw_df %>%
            dplyr::filter( FullPeptideName %in% uni_mod | FullPeptideName %in% unimodTocodename(uni_mod) ) %>%
            dplyr::select( Charge ) %>%
            as.matrix() %>%
            mstools::Mode() -> Isoform_Target_Charge
          
          if ( length(Isoform_Target_Charge)>1 ){
            
            if ( is.null(Charge_State) ){
              MazamaCoreUtils::logger.info(paste('* There are ', length(Isoform_Target_Charge), ' charge states.. Will Randomly sample one of the charge states...\n', sep=''))
              Isoform_Target_Charge <- sample(Isoform_Target_Charge,1) # @ CHANGEEE
            } else {
              Isoform_Target_Charge <- Charge_State
            }
            MazamaCoreUtils::logger.info(paste('* Will analyze peptidoform with charge state: ', (Isoform_Target_Charge), '\n', sep=''))
          }
          
          # Filter df_lib based on only the uni modifications with specified charge state found in OSW results
          df_lib %>%
            dplyr::filter( PRECURSOR_CHARGE==Isoform_Target_Charge ) -> df_lib
          
          MazamaCoreUtils::logger.info(paste('Peptidoforms of Charge State ', Isoform_Target_Charge, ' to Analyze..\n', paste(uni_mod, collapse = '\n'), '\n', sep=''))
          if ( length(uni_mod)==1 & N_sample!=1 ){ MazamaCoreUtils::logger.error(crayon::red('There is only 1 form... Skipping...\n')); skipped_bool=TRUE; next }
          
          
          # Display other peak group rank features
          if ( show_all_pkgrprnk==T ){
            if ( is.null(.osw_df) ){
              osw_df_all <- getOSWData_( in_osw, run_name, precursor_id='', peptide_unmodified = pep, mod_residue_position='', peak_group_rank_filter=F, pep_list='', ipf_filter='', ms2_score=T, ipf_score=F )
            } else {
              osw_df_all <- .osw_df
              MazamaCoreUtils::logger.info( "A data.frame object containing peak group scoring data was supplied" )
            }
            osw_df_all %>%
              dplyr::filter( grepl( paste0(".*", run_name, ".*"), filename) ) %>%
              dplyr::filter( FullPeptideName %in% uni_mod) %>%
              dplyr::filter( Charge %in% Isoform_Target_Charge )-> osw_df_all_filtered
            
            RT_Table <- table( osw_df_all_filtered$RT )
            
            # RT_pkgrps <- as.numeric(names(RT_Table)[RT_Table==2])
            RT_pkgrps <- as.numeric(names(RT_Table))
            if ( length(RT_pkgrps)==0 ){
              MazamaCoreUtils::logger.warn(crayon::red('WARNING: There were no common RT pkgrps found, will plot all pkgrps...\n'))
              RT_pkgrps <- as.numeric(names(RT_Table))
            }
            # rm(osw_df_all, osw_df_all_filtered, RT_Table)
            
          } else {
            RT_pkgrps <- NULL
          }
          
          ###############################
          ## Get Site Determining Ions ##
          ###############################
          # cat(paste('   ~ Getting site Determining Ions information for each peptidoform\n', sep=''))
          # if ( N_sample!=1 ){
          #   print(uni_mod)
          #   print(len_unmod) 
          #   print(N_sample)
          #   print('getPairSiteDeterminingIonInformation_')
          #   uni_mod_list <- getPairSiteDeterminingIonInformation_( uni_mod, len_unmod )
          # } else {
          #   print(uni_mod)
          #   print(len_unmod)
          #   print(N_sample)
          #   print('getSiteDeterminingIonInformation_')
          #   uni_mod_list <- getSiteDeterminingIonInformation_( uni_mod, len_unmod )
          # }
          # ## Check for errors in uni_mod_list
          # if ( suppressWarnings( uni_mod_list=='skip' ) ){ cat('skipping...\n'); next }
          uni_mod_list <- NULL
          
          if ( plotIntersectingDetecting==T & plotUniqueDetecting==T ){
            # df_lib %>%
            #   dplyr::filter( MODIFIED_SEQUENCE==uni_mod[1] ) %>%
            #   dplyr::filter( DETECTING==1 ) -> df1
            # 
            # df_lib %>%
            #   dplyr::filter( MODIFIED_SEQUENCE==uni_mod[2] ) %>%
            #   dplyr::filter( DETECTING==1 ) -> df2
            # 
            # intersecting_mz <- intersect(df1$PRODUCT_MZ, df2$PRODUCT_MZ)
            
            product_mz <- unlist( lapply(uni_mod, function( mod_sequence ){ 
              df_lib %>%
                dplyr::filter( MODIFIED_SEQUENCE==mod_sequence ) %>%
                dplyr::filter( DETECTING==1 ) %>%
                dplyr::select( PRODUCT_MZ ) %>%
                as.matrix() %>%
                as.numeric()
            }) )
            
            intersecting_mz <- product_mz[table(product_mz)>1]
          } else {
            intersecting_mz <- NULL
          }
          
          #######################################
          ## Get Top Transitions with good PEP ##
          #######################################
          if ( use_top_trans_pep==T ){
            tictoc::tic(paste('   ~ Getting Top Transitions that scored a low PEP', sep=''))
            Transition_Scores <- getTransitionScores_( in_osw, run_name, precursor_id='', peptide_id=pep )
            
            if( is.null(unique(unlist(Transition_Scores$transition_id))) ){ MazamaCoreUtils::logger.error(crayon::red('These is no Transition ID information... Skipping... \n')); next  }
            
            top_trans_mod_list <- lapply(uni_mod, function(mod, df_lib, Transition_Scores){
              df_lib %>%
                dplyr::filter( MODIFIED_SEQUENCE == mod ) -> mod_df_lib
              
              mod_df_lib %>%
                dplyr::filter( MODIFIED_SEQUENCE==mod ) %>%
                dplyr::filter( DETECTING==0 ) -> mod_df_lib_filtered
              
              Transition_Scores %>%
                dplyr::filter( FullPeptideName == mod) %>%
                dplyr::filter( !is.nan(transition_id) ) %>%
                dplyr::filter( transition_id %in% mod_df_lib_filtered$TRANSITION_ID ) %>%
                dplyr::distinct() %>%
                dplyr::arrange( transition_pep ) -> mod_transition_scores
            }, df_lib, Transition_Scores)
            names(top_trans_mod_list) <- uni_mod
            tictoc::toc()
          } else {
            top_trans_mod_list <- NULL
          }
          
          #######################################
          ## Get TRANSITION SCORES INFO TABLE  ##
          #######################################
          if ( show_transition_scores ){
            transition_dt <- getTransitionScores_( oswfile = in_osw, run_name = run_name, precursor_id = "", peptide_id = pep)
          } else {
            transition_dt <- NULL
          }
          
          MazamaCoreUtils::logger.info('   ~ Starting Plotting Action\n', sep='')
          plot_list <- list()
          max_Int <- 0
          uni_mod <- sort(uni_mod)
          for( mod in uni_mod ){ # names(uni_mod_list) ){
            MazamaCoreUtils::logger.info( crayon::green('   --- Peptidoform: ', mod), '\n', sep='')
            ###########################
            ##     PLOT PRECURSOR    ##
            ###########################
            if ( plotPrecursor==T ){
              
              g <- ggplot2::ggplot()
              g <- mstools::getXIC( graphic_obj = g, 
                                    df_lib = df_lib, 
                                    mod = mod, 
                                    Isoform_Target_Charge = Isoform_Target_Charge,
                                    chromatogram_file = in_sqMass,  
                                    transition_type = 'precursor', 
                                    uni_mod_list = uni_mod_list, 
                                    max_Int = max_Int, 
                                    retention_time_limits=retention_time_limits,
                                    in_osw=NULL, 
                                    smooth_chromatogram=smooth_chromatogram, 
                                    doFacetZoom=F, 
                                    top_trans_mod_list=NULL, 
                                    show_n_transitions=show_n_transitions,
                                    transition_dt=transition_dt )
              max_Int <- g$max_Int
              g <- g$graphic_obj
            } else {
              g <- ggplot2::ggplot()
            }
            
            #################################
            ##   DETECTING TRANSITIONS     ##
            #################################
            
            ## INTERSECTING
            if ( plotIntersectingDetecting==T ){
              g <- mstools::getXIC( graphic_obj = g, 
                                    df_lib = df_lib, 
                                    mod = mod, 
                                    Isoform_Target_Charge = Isoform_Target_Charge,
                                    chromatogram_file = in_sqMass, 
                                    transition_type = 'detecting', 
                                    uni_mod_list = uni_mod_list, 
                                    max_Int = max_Int, 
                                    retention_time_limits=retention_time_limits,
                                    in_osw=NULL, 
                                    smooth_chromatogram=smooth_chromatogram, 
                                    doFacetZoom=F, 
                                    top_trans_mod_list=NULL, 
                                    show_n_transitions=show_n_transitions,
                                    transition_dt=transition_dt )
              max_Int <- g$max_Int
              g <- g$graphic_obj
            }
            ## UNIQUE
            if ( plotUniqueDetecting==T ){
              g <- mstools::getXIC( graphic_obj = g, 
                                    df_lib = df_lib, 
                                    mod = mod, 
                                    Isoform_Target_Charge = Isoform_Target_Charge,
                                    chromatogram_file = in_sqMass, 
                                    transition_type='detecting_unique', 
                                    uni_mod_list = uni_mod_list, 
                                    max_Int = max_Int, 
                                    retention_time_limits=retention_time_limits,
                                    in_osw=NULL, 
                                    smooth_chromatogram=smooth_chromatogram, 
                                    doFacetZoom=F, 
                                    top_trans_mod_list=NULL, 
                                    show_n_transitions=show_n_transitions,
                                    transition_dt=transition_dt )
              max_Int <- g$max_Int
              g <- g$graphic_obj
            }
            ###################################
            ##    IDENTIFYING TRANSITIONS   ###
            ###################################
            if ( plotIdentifying==T ){
              g <- mstools:: getXIC( graphic_obj = g, 
                                    df_lib = df_lib, 
                                    mod = mod, 
                                    Isoform_Target_Charge = Isoform_Target_Charge,
                                    chromatogram_file = in_sqMass,  
                                    transition_type='identifying', 
                                    uni_mod_list = uni_mod_list, 
                                    max_Int = max_Int, 
                                    retention_time_limits=retention_time_limits,
                                    in_osw=NULL, 
                                    smooth_chromatogram=smooth_chromatogram, 
                                    doFacetZoom=F, 
                                    top_trans_mod_list=top_trans_mod_list, 
                                    transition_selection_list=transition_selection_list,
                                    show_n_transitions=show_n_transitions, 
                                    plotIdentifying.Unique=plotIdentifying.Unique, 
                                    plotIdentifying.Shared=plotIdentifying.Shared, 
                                    plotIdentifying.Against=plotIdentifying.Against,
                                    transition_dt=transition_dt )
              max_Int <- g$max_Int
              g <- g$graphic_obj
              
            } else {
              MazamaCoreUtils::logger.warn(crayon::red('-- Identifying Transitions were not found for: ', crayon::underline(mod)), '\n', sep='')
            }
            
            ###################################
            ##     ADD OSW RESULTS INFO     ###
            ###################################
            tryCatch( 
              expr = {
                g <- mstools::getXIC( graphic_obj = g, 
                                      df_lib = df_lib, 
                                      df_osw = osw_df,
                                      mod = mod, 
                                      Isoform_Target_Charge = Isoform_Target_Charge,
                                      chromatogram_file = in_sqMass, 
                                      transition_type='none', 
                                      uni_mod_list = uni_mod_list, 
                                      max_Int = max_Int, 
                                      in_osw = in_osw, 
                                      SCORE_IPF=SCORE_IPF,
                                      annotate_best_pkgrp=annotate_best_pkgrp,
                                      doFacetZoom=doFacetZoom, 
                                      top_trans_mod_list=NULL, 
                                      RT_pkgrps=RT_pkgrps, 
                                      show_manual_annotation=show_manual_annotation, 
                                      show_peak_info_tbl=show_peak_info_tbl,
                                      FacetFcnCall=FacetFcnCall, 
                                      show_legend = show_legend  )
              },
              error = function(e){
                MazamaCoreUtils::logger.error(sprintf('[mstools::XICMasterPlotFcn] There was an issue while adding OpenSWATH information to XIC plot:\n%s', e$message))
              })
            max_Int <- g$max_Int
            g <- g$graphic_obj
            
            plot_list[[mod]] <- g
          } 
          graphics.off()
          final_g <- (gridExtra::arrangeGrob(grobs=plot_list, nrow=length(uni_mod)))
          final_g_list[[uni_isoform_group_list_idx]] <- final_g
          final_g_list <- plot_list # TODO: Check if this is right 
          # grid.draw(final_g)
          
        } # uni_isoform_group_list_idx
        
        return( final_g_list )
        # record_list[[record_idx]] <- final_g
        # record_idx <- record_idx + 1
      }, error=function(e){
        if (grepl("reached elapsed time limit|reached CPU time limit", e$message)) {
          # we reached timeout, apply some alternative method or do something else
          MazamaCoreUtils::logger.error(crayon::red('Reached CPU Timelimit for ', crayon::underline(pep), ' from run: '), crayon::underline(run), '\n', sep='')
        } else {
          # error not related to timeout
          MazamaCoreUtils::logger.error(sprintf('[mstools::XICMasterPlotFcn] There was an issue trying to process %s from run %s:\n%s', crayon::underline(pep), crayon::underline(run), e$message) )
        }
      }
      )
      
    }, mc.cores = 1 ) ## mclapply 
    # TODO: Workaround mc.core = 1 for windows, or remove parallel, might not be necessary here.
    ##*****************************
    ##    Save/Print Plot
    ##*****************************
    if( length(record_list)>0){
      draw_chrom_error <- tryCatch({
        if( doPlot==T & length(record_list[[1]])!=0 ){

          ### RECORD
          store_record <- list()
          store_idx <- 1
          for ( grph_idx in seq(1,length(record_list),1) ){
            if ( !is.null(record_list[[grph_idx]]) ){
              if ( length(record_list[[grph_idx]])>1 ){
                ## There could be multiple isoform groups for 1 peptide in 1 run
                for ( sub_record_idx in seq(1,length(record_list[[grph_idx]]),1) ){
                  grid::grid.draw(record_list[[grph_idx]][[sub_record_idx]])
                  store_record[[store_idx]] <- recordPlot()
                  store_idx <- store_idx + 1
                  graphics.off()
                }
              } else {
                grid::grid.draw(record_list[[grph_idx]][[1]])
                store_record[[store_idx]] <- recordPlot()
                store_idx <- store_idx + 1
                graphics.off()
              }
            }
          }
          
          ## Save plot as PDF
          if ( !is.null(store_plots_subdir) ){
          ## Check to see if save directory exits
          dir.create(file.path(getwd(), store_plots_subdir), showWarnings = FALSE )
          
          message( sprintf("Plots will be saved in: %s", file.path(getwd(), store_plots_subdir)) )
          
          # write and Append plots to a pdf file
          datatime_log <- sub(':', 'm', sub(':', 'h', gsub(' ', '_', as.character(as.POSIXct(Sys.time())))))
          pdf(paste(getwd(),store_plots_subdir,pep,'_ChargeState_', Charge_State, '_Precursor_', plotPrecursor, '_Detecting_', plotIntersectingDetecting, '_IdenifyingUnique_', plotIdentifying.Unique, '_IdentifyingShared_', plotIdentifying.Shared, '_IdentifyingAgainst_', plotIdentifying.Against, '_', datatime_log, '.pdf',sep=''), onefile = T, width=120, height=120, paper='a4r', pointsize=0.5 )
          for (myplot in store_record){
            if (class(myplot)=='recordedplot'){
              replayPlot(myplot)
            }
            
          }
          graphics.off()
          }
          
          ## Print Plot to Graphics
          if ( printPlot==T ){
            for (myplot in store_record){
              if (class(myplot)=='recordedplot'){
                replayPlot(myplot)
              }
              
            }
          }
        } #doPlot END BLOCK
      }, error=function(e){
        # error not related to timeout
        MazamaCoreUtils::logger.error(crayon::red('There was an issue trying to draw ', crayon::underline(pep)), '\n', sep='')
      })
    }# length of record plot list chec
  }
  tictoc::toc()
  return( record_list[[1]][[1]] )
}