R/plot_contaminants.R

In svalvaro/MQanalyser: Analyse the proteomics LC-MS/MS data from MaxQuant.

#' Title
#'
#' @param proteoInput
#' @param intensityType
#'
#'
#' @return
#' @export
#'
#' @examples
plot_contaminants <- function(proteoInput,
                              intensityType = 'LFQ',
                              softwareUsed = 'Spectronaut',
                              interactive = FALSE
                              ){

    if (softwareUsed == 'MaxQuant') {

        # df <- proteoInput %>% select(contains(
        #     c( 'Contaminant', 'LFQ','Gene.names')))
        df <- proteoInput %>% select(contains(
            c( 'Contaminant', intensityType,'Gene.names')))

        names(df) <- gsub(paste0(intensityType,'.intensity.'),'',names(df))
    }

    if (softwareUsed == 'Spectronaut') {

        if (intensityType == 'LFQ') {

            df <- proteoInput %>% select(contains(
                c( 'Contaminant', 'PG.Quantity','Gene.names')))

            columns <-  grep('PG.Quantity', colnames(df))

            # remove the ending of the names
            colnames(df)[columns] <-  gsub(pattern = '.raw.PG.Quantity','',base::colnames(df)[columns])

        }

        if (intensityType == 'iBAQ') {

            df <- proteoInput %>% select(contains(
                c( 'Contaminant', 'PG.IBAQ','Gene.names')))

            columns <-  grep('PG.IBAQ', colnames(df))

            # remove the ending of the names
            colnames(df)[columns] <-  gsub(pattern = '.raw.PG.IBAQ','',base::colnames(df)[columns])
        }

        colnames(df)[columns] <-  gsub(pattern = '\\[.*\\] ','',base::colnames(df)[columns])

    }

    df2 <- reshape2::melt(df, id.var = c('Potential.contaminant','Gene.names'))

    df2 <- df2[!is.na(df2$value),]

    # Change the + for Contaminat or Not Contaminant
    df2$Potential.contaminant <- ifelse(df2$Potential.contaminant == '+',
                                        'Contaminant', 'Not Contaminant')

    # Log2 Value, and removed -Inf
    df2$value <- log2(as.numeric(df2$value))

    df2 <- df2[!df2$value == '-Inf',]

    colnames(df2)[colnames(df2)=='value'] <- 'Log2.Intensity'

    # Colors for the plot

    if (length(unique(df2$Potential.contaminant)) == 2) {
        colors <- c('#dd4b39', '#71a873')
    }else{
        # colors <- '#FFDFD3'
        colors <- '#71a873'
    }

    p <- ggplot(df2, aes( y = Log2.Intensity,  key = Gene.names))+
            #gghalves::geom_half_point()+
            geom_jitter(aes(x = Potential.contaminant,
                            colour = Potential.contaminant))+
            facet_wrap(.~variable)+
            ggtitle('Intensity distribution')+
            ylab('Log2 Intensity')+
            xlab('')+
            theme_bw()+
            theme(
                axis.text.x = element_blank(),
                axis.ticks.x = element_blank(),
                legend.title = element_blank()
                )+
            scale_colour_manual(values = colors)

    if (interactive == TRUE) {
        return(
            ggplotly(p, tooltip = c('y', 'key', 'colour'))
        )
    }else{
        return(
            p
        )
    }
}