R/plot.ChemoProtSet.R

In brunocontrino/Doscheda: A DownStream Chemo-Proteomics Analysis Pipeline

#' Default plot for objects of class ChemoProtSet
#'
#'Description
#'
#' @param x object of class 'ChemoProtSet'
#' @param sigmoidCoef the sigmoidal coeffcient, one of ('difference', 'slope', 'rb50'). Obselete if modelType is 'linear'
#' @param ... other plotting options
#' @return plot for objects of class ChemoProtSet
#' @import ggplot2
#' @import gridExtra
#' @import reshape2
#' @export

plot.ChemoProtSet <- function(x, sigmoidCoef = "rb50", ...) {
    
    inherits(x, "ChemoProtSet")
    modParams <- getParameters(x)
    if (modParams$modelType == "linear") {
        data.merged <- getFinal(x)
        ..count.. <- NULL
        m0 <- ggplot2::ggplot(data.merged, ggplot2::aes(x = data.merged$P.Value_slope))
        m0 <- m0 + geom_histogram(ggplot2::aes(fill = ..count..), binwidth = 0.01) + ggplot2::scale_fill_gradient("Count", 
            low = "green", high = "red") + ggplot2::xlab("P.val slope")
        
        ..count.. <- NULL
        m1 <- ggplot2::ggplot(data.merged, ggplot2::aes(x = data.merged$P.Value_intercept))
        m1 <- m1 + geom_histogram(ggplot2::aes(fill = ..count..), binwidth = 0.01) + ggplot2::scale_fill_gradient("Count", 
            low = "green", high = "red") + ggplot2::xlab("Pval intercept")
        
        m2 <- ggplot2::ggplot(data.merged, ggplot2::aes(x = data.merged$P.Value_quadratic))
        m2 <- m2 + geom_histogram(ggplot2::aes(fill = ..count..), binwidth = 0.01) + ggplot2::scale_fill_gradient("Count", 
            low = "green", high = "red") + ggplot2::xlab("Pval quadratic")
        
        gridExtra::grid.arrange(m0, m1, m2)
        
    } else {
        
        data_merged_2 <- getFinal(x)
        conc <- modParams$sigmoidConc
        topperc <- 30
        # difference in % between top and bottom
        
        if (modParams$dataType == "intensity") {
            
            pred.names <- paste0("predX", 1:(modParams$chans - 1))
            final.Names <- paste0("rep1_C", 0:(modParams$chans - 2))
            diffinter <- data_merged_2[(data_merged_2$predX1 - data_merged_2[, paste("predX", (modParams$chans - 
                1), sep = "")]) > topperc & data_merged_2$predX1 <= 100, ]
            
        } else {
            pred.names <- paste0("predX", 1:(modParams$chans))
            final.Names <- paste0("rep1_C", 0:(modParams$chans - 1))
            diffinter <- data_merged_2[(data_merged_2$predX1 - data_merged_2[, paste("predX", (modParams$chans), 
                sep = "")]) > topperc & data_merged_2$predX1 <= 100, ]
            
        }
        
        
        if (sigmoidCoef == "difference") {
            
            if (nrow(diffinter) > 0) {
                Diff_Top_bottom_pred <- shape_for_ggplot_pred(diffinter, log2(conc), pred.names)
                Diff_Top_bottom_perc <- shape_for_ggplot_perc(diffinter, log2(conc), final.Names)
                what <- c("(Top - Bottom) >")
                GeneID <- factor(Diff_Top_bottom_pred$GeneID)
                value <- NULL
                Diff_Top_bottom <- ggplot2::ggplot() + ggplot2::geom_line(data = Diff_Top_bottom_pred, 
                  ggplot2::aes(x = x, y = value, colour = GeneID), size = 1) + ggplot2::geom_point(data = Diff_Top_bottom_perc, 
                  ggplot2::aes(x = x, y = value, colour = Diff_Top_bottom_perc$GeneID)) + ggplot2::labs(title = paste(what, 
                  topperc, sep = ""))
                
                Diff_Top_bottom
            } else {
                Diff_Top_bottom <- ggplot2::ggplot() + ggplot2::labs(title = paste("No significant Top-Bottom >", 
                  topperc, "%", "\n", "has been found", sep = ""))
            }
        } else if (sigmoidCoef == "slope") {
            
            ## next plot (SLOPE)
            
            top <- 15
            
            if (modParams$dataType == "intensity") {
                pred.names <- paste0("predX", 1:(modParams$chans - 1))
                final.Names <- paste0("rep1_C", 0:(modParams$chans - 2))
            } else {
                pred.names <- paste0("predX", 1:(modParams$chans))
                final.Names <- paste0("rep1_C", 0:(modParams$chans - 1))
            }
            
            data_merged_2 <- getFinal(x)
            
            # Here make the subselections for using the ggplot functions SLOPE
            slope <- stats::na.omit(data_merged_2[data_merged_2$SlopePval < 0.05, ])
            slope_ordered <- stats::na.omit(slope[order(slope$SlopePval, decreasing = FALSE), ][1:top, 
                ])
            if (nrow(slope_ordered) > 0) {
                slope_pred <- shape_for_ggplot_pred(slope_ordered, log10(conc), pred.names)
                slope_perc <- shape_for_ggplot_perc(slope_ordered, log10(conc), final.Names)
                what <- c("Slope (p.val) ")
                GeneID <- factor(slope_pred$GeneID)
                Slope_pl <- ggplot2::ggplot() + ggplot2::geom_line(data = slope_pred, ggplot2::aes(x = x, 
                  y = value, colour = GeneID), size = 1) + ggplot2::geom_point(data = slope_perc, 
                  ggplot2::aes(x = x, y = value, colour = slope_perc$GeneID)) + ggplot2::labs(title = paste(what, 
                  "Top", top, sep = ""))
                Slope_pl
            } else {
                Slope_pl <- ggplot2::ggplot() + ggplot2::labs(title = "No significant Sigmoidal Slope has been found")
            }
            
        } else if (sigmoidCoef == "rb50") {
            
            ##### next plot (RB50)
            
            
            top <- 15
            
            RB50 <- data.frame(stats::na.omit(data_merged_2[data_merged_2$RB50Err < as.numeric(summary(data_merged_2$RB50Err)[5]) & 
                data_merged_2$RB50Pval < 0.05 & data_merged_2$predX1 - data_merged_2[, paste0("predX", 
                (modParams$chans - 1))] > 0 & data_merged_2$predX1 <= 100, ]))
            
            
            RB50_ordered <- stats::na.omit(RB50[order(RB50$RB50Pval, decreasing = FALSE), ][1:top, 
                ])
            
            if (nrow(RB50_ordered) > 0) {
                RB50_pred <- shape_for_ggplot_pred(RB50_ordered, log10(conc), pred.names)
                RB50_perc <- shape_for_ggplot_perc(RB50_ordered, log10(conc), final.Names)
                what <- c("RB50 (p.val) ")
                GeneID <- factor(RB50_pred$GeneID)
                RB50_pl <- ggplot2::ggplot() + ggplot2::geom_line(data = RB50_pred, ggplot2::aes(x = x, 
                  y = value, colour = GeneID), size = 1) + ggplot2::geom_point(data = RB50_perc, 
                  ggplot2::aes(x = x, y = value, colour = RB50_perc$GeneID)) + ggplot2::labs(title = paste(what, 
                  "Top", top, sep = ""))
                RB50_pl
            } else {
                RB50_pl <- ggplot2::ggplot() + ggplot2::labs(title = "No significant RB50 has been found")
                print(RB50_pl)
            }
        } else {
            message("sigmoidCoef not accepted please enter one of: \"difference\", \"slope\" or \"rb50\"")
        }
    }
}