R/PlotPTMAcrossSamples.R

In svalvaro/MQmetrics: Quality Control of Protemics Data

#' Plot PTM across samples
#'
#' @param MQCombined Object list containing all the files from the MaxQuant
#' output. It is the result from using \code{make_MQCombined}.
#' @param PTM_of_interest Post-Translation Modification of interest. It is
#' important they are defined exactly as MaxQuant does:
#' Examples:
#' 'Oxidation (M)', 'Acetyl (Protein N-term)', 'Unmodified', etc.
#' @param  log_base The logarithmic scale for the intensity. Default is 2.
#' @param long_names If TRUE, samples having long names will be considered, and
#'  the name will be split by sep_names. By default = FALSE.
#' @param sep_names If long_names is TRUE, sep_names has to be selected. Samples
#'  names will be split. By default is NULL.
#'
#'
#' @return A plot showing the PTM of interest.
#' @export
#'
#' @examples
#' MQPathCombined <- system.file("extdata/combined/", package = "MQmetrics")
#' MQCombined <- make_MQCombined(MQPathCombined)
#' PlotPTMAcrossSamples(MQCombined, PTM_of_interest = 'Oxidation (M)')
PlotPTMAcrossSamples <- function(MQCombined,
                                PTM_of_interest = 'Oxidation (M)',
                                log_base = 2,
                                long_names = FALSE,
                                sep_names = NULL){

    variable <- value <- Modifications <- NULL

    all_plots <- list()

    for(index in seq_len(length(PTM_of_interest))){

        df <- MQCombined$modificationSpecificPeptides.txt

        df <- df[grepl(PTM_of_interest[index], df$Modifications, fixed = TRUE),]

        if(nrow(df)==0){
            print('PTM provided not found')
            print('Did you write it correctly?')

        }else{
            df <- df %>%  select(contains(c(
                "Modifications", "Proteins", "Intensity "))
            ) %>%
                select(-contains(c("calibrated", "Unique (Proteins)",
                                   'Proteins')))


            df_melted <- melt(df, id.vars = 'Modifications')

            #remove the name Intensity
            df_melted$variable <- gsub('Intensity','', df_melted$variable)

            #Rremove values = 0
            df_melted <- df_melted[df_melted$value != 0, ]

            #apply log
            df_melted$value <- log(df_melted$value, base = log_base)

            # Rename the modifications, to aggrupate them into the modification
            # of interest

            df_melted$Modifications <- PTM_of_interest[index]

            p <-    ggplot(df_melted, aes(x = variable, y = value,
                                          fill = Modifications))+
                gghalves::geom_half_violin(side = 'r',
                                           position = position_nudge(
                                               x = 0.25,y = 0),
                                           adjust = 2, trim = FALSE,
                                           alpha = 0.4,
                                           fill = '#FEE715FF')+
                geom_jitter(width = 0.2, alpha = 0.1, color = '#101820FF')+
                geom_boxplot(width = 0.07, alpha= 0.1,
                             position = position_nudge(x = 0.29, y = 0),
                             outlier.shape = NA,
                             fill = '#FEE715FF')+
                theme_bw()+
                ggtitle(paste0('Intensities of peptides with: ',
                               PTM_of_interest[index]))+
                xlab('Experiment')+
                ylab(paste0('Log',log_base,' of Intensity'))+
                theme(legend.position = 'none')


            if (long_names == TRUE) {
                p <- p + scale_x_discrete(labels = function(x) {
                    stringr::str_wrap(gsub(sep_names," ", x),3)})
            }
            #return(p)

            all_plots[[index]] <- p
        }
    }
return(all_plots)

}