R/plot_quantlim.R

In Vitek-Lab/MSstatsLODQ: Assay characterization: estimation of limit of blanc(LoB) and limit of detection(LOD)

#' Plot results of nonlinear_quantlim() and linear_quantlim()
#' 
#' This function allows to plot the curve fit that is used to calculate the LOB 
#' and LOD with functions nonlinear_quantlim() and linear_quantlim(). The 
#' function outputs for each calibration curve, two pdf files each containg one 
#' plot. On the first, designated by _overall.pdf, the entire concentration 
#' range is plotted. On the second plot, designated by _zoom.pdf, the 
#' concentration range between 0 and xlim_plot (if specified in the argument 
#' of the function) is plotted. When no xlim_plot value is specified, the 
#' region close to LOB and LOD is automatically plotted.
#' 
#' @param spikeindata Data frame that contains the experimental spiked in data. 
#' This data frame should be identical to that used as input by function 
#' functions nonlinear_quantlim() or linear_quantlim().  The data frame has to 
#' contain the following columns : CONCENTRATION, INTENSITY (both of which are 
#' measurements from the spiked in experiment) and NAME which designates the 
#' name of the assay (e.g. the name of the peptide or protein)
#' @param quantlim_out Data frame that was output by functions 
#' nonlinear_quantlim() or linear_quantlim(). It has to contain at least the 
#' following columns: i) CONCENTRATION: Concentration values at which the value 
#' of the fit is calculated ii) MEAN: The value of the curve fit iii) LOW: The 
#' value of the lower bound of the 95\% prediction interval iv) UP: The value of
#'  the upper bound of the 95\% prediction interval v) LOB: The value of the LOB
#'   (one column with identical values) vi) LOD: The value of the LOD (one 
#'   column with identical values) vii) NAME: The name of the assay (identical 
#'   to that provided in the input) viii) METHOD which is LINEAR or NONLINEAR
#' @param alpha Probability level to estimate the LOB/LOD
#' @param xlim_plot Optional argument containing the maximum xaxis value of the 
#' zoom plot. When no value is specified, a suitable value close to LOD is 
#' automatically chosen.
#' @param width width of the saved file. Default is 10.
#' @param height height of the saved file. Default is 10.
#' @param address the name of folder that will store the results. Default
#' folder is the current working directory. The other assigned folder has to
#' be existed under the current working directory. An output pdf file is
#' automatically created with the default name of "QuantLim.pdf" and
#' "QuantLim_Zoom.pdf". The command address can help to specify where to store 
#' the file as well as how to modify the beginning of the file name. If
#' address=FALSE, plot will be not saved as pdf file but showed in window
#' 
#' @return list of two ggplot2 object
#' 
#' @importFrom grDevices dev.off hcl pdf
#' @import ggplot2
#' @export
#' 
#' @examples
#' 
#' ## Run LOBD analysis and plot
#' quant_out = nonlinear_quantlim(spikeindata, Nbootstrap = 10)
#' plot_quantlim(spikeindata = spikeindata, quantlim_out = quant_out, 
#'                address = FALSE)
#'                
plot_quantlim = function(spikeindata, 
                          quantlim_out, 
                          alpha, 
                          xlim_plot, 
                          width = 12,
                          height = 4, 
                          address = ""){
  
  if (is.null(quantlim_out)) {
    msg <-  ("Assay fit was incorrectly calculated by linear_quantlim or nonlinear_quantlim and cannot be plotted")
    stop(msg)
  }
  
  #percentile of the prediction interval considered
  if (missing(alpha)) {
    alpha <- 5/100
  }
  
  if (alpha >= 1 | alpha  <= 0) {
    msg <- ("incorrect specified value for alpha,  0 < alpha < 1")
    stop(msg)
  }
  
  expdata <- spikeindata
  
  datain <- quantlim_out
  #Define some colors here for the plots:
  black1 <- '#000000' 
  orange1 <- "#E69F00"
  blue1 <- "#56B4E9"
  green1 <- "#009E73"
  yellow1 <- "#F0E442"
  blue2 <- "#0072B2"
  red1 <- "#D55E00"
  pink1 <- "#CC79A7"
  cbbPalette <- c(black1, orange1, blue1, green1, yellow1, blue2 ,red1 , pink1)
  
  #Rename variables for the function:
  names(datain)[names(datain) == 'CONCENTRATION'] <- 'C'
  names(expdata)[names(expdata) == 'CONCENTRATION'] <- 'C'
  names(expdata)[names(expdata) == 'INTENSITY'] <- 'I'
  names(datain)[names(datain) == 'MEAN'] <- 'mean'
  names(datain)[names(datain) == 'LOW'] <- 'low'
  names(datain)[names(datain) == 'UP'] <- 'up'
  
  #Remove NA and infinite numbers from spike in data:
  expdata <- expdata[!is.na(expdata$I) & !is.na(expdata$C),]  
  expdata <- expdata[!is.infinite(expdata$I) & !is.infinite(expdata$C),]  
  
  
  #Extract actual data points for plotting:
  Cdata <- expdata$C
  Idata <- expdata$I 
  
  tmp_blank <- expdata[expdata$C == 0,]
  n_blank <- length(unique(tmp_blank$I))
  noise <- mean(tmp_blank$I)
  var_noise <- var(tmp_blank$I)
  
  fac <- qt(1-alpha,n_blank - 1)*sqrt(1+1/n_blank)
  
  #upper bound of noise prediction interval
  up_noise <- noise + fac * sqrt(var_noise)
  
  rel_size <- 2.5
  rel_size_2 <- 1.8
  lw <- 1
  pw <- 2.5
  
  xaxis_orig_2 <- datain$C
  tmp_all <- datain
  LOQ_pred <- datain$LOD[1]
  LOD_pred <- datain$LOB[1]
  
  lower_Q_pred <- datain$low
  upper_Q_pred <- datain$up
  mean_bilinear <- datain$mean
  
  if (LOD_pred >= 0) {
    y_LOD_pred <- up_noise
  }
  if (LOQ_pred >= 0) {
    y_LOQ_pred <- up_noise
  }  
  
  if (address != FALSE) {
    allfiles <- list.files()
    
    num <- 0
    filenaming <- paste0(address, "QuantLim")
    finalfile <- paste0(address, "QuantLim.pdf")
    
    while (is.element(finalfile, allfiles)) {
      num <- num + 1
      finalfile <- paste0(paste(filenaming, num, sep = "-"), ".pdf")
    }
    
    pdf(finalfile, width = width, height = height)
  }
  
  if (LOQ_pred > xaxis_orig_2[3]) {
    C_max <- xaxis_orig_2[min(which(abs(LOQ_pred - xaxis_orig_2) == min(
      abs(LOQ_pred - xaxis_orig_2))) +1, length(xaxis_orig_2))]      
  }else{
    C_max <- xaxis_orig_2[which(abs(mean(xaxis_orig_2) - xaxis_orig_2) == min(
      abs(mean(xaxis_orig_2) - xaxis_orig_2)))]
  }
  
  low_p <- paste0(alpha * 100, "%")
  high_p <- paste0(100 - alpha * 100, "%")
  upp_noise <- paste(high_p, " upper bound of noise")
  low_pred <- paste(low_p, 'percentile of predictions') 
  
  plots_list <- vector("list", 2)
  
  p1 <- ggplot() + .theme_complete_bw()
  p1 <- p1 + geom_point(data=data.frame(Cdata,Idata) , aes(x=Cdata,y=Idata), 
                        size =pw*1.5)
  p1 <- p1 + geom_line(data=data.frame(x=xaxis_orig_2, y=mean_bilinear, 
                                       col='mean prediction', lt = 'mean'),
                       aes_string(x="x", y="y", color="col"), size = lw)
  p1 <- p1 + geom_ribbon(data=data.frame(x=xaxis_orig_2, ymin =lower_Q_pred, 
                                         ymax =upper_Q_pred), 
                        aes_string(x="x", ymin="ymin", ymax="ymax"), fill = red1, 
                         alpha = 0.3)
  p1 <- p1 + geom_line(data = data.frame(x=xaxis_orig_2, ymin =lower_Q_pred, 
                                         col = low_pred, lt = 'Int'), 
                      aes_string(x="x", y="ymin", color = "col"), size = lw)
  p1 <- p1 + geom_line(data=data.frame(x=xaxis_orig_2, ymax = rep(
    up_noise,length(xaxis_orig_2)), col = "95% upper bound of noise"),
    aes_string(x="x", y="ymax", color = "col"),
    size  = lw)
  p1 <- p1 + scale_alpha_continuous(guide = 'none') 
  p1 <- p1 + xlab('Spiked Concentration') + ylab('Estimated Concentration')
  p1 <- p1 + theme(axis.text.x = element_text(size = rel(rel_size))) + 
    theme(axis.text.y = element_text(size = rel(rel_size)))
  p1 <- p1 + theme(axis.title.x = element_text(size = rel(rel_size))) + 
    theme(axis.title.y = element_text(size = rel(rel_size)))
  p1 <- p1 + theme(axis.title.y=element_text(vjust=0.7))
  
  p1 <- p1 + scale_color_manual(values = c(orange1, blue1, red1),
                                labels = c(low_pred,
                                           upp_noise,
                                           "mean prediction"))
  
  p1 <- p1 + theme(legend.title = element_blank()) + 
    theme(legend.position = c(0.05, 0.5), 
          legend.justification = c(0, 0), 
          legend.text=element_text(size=rel(rel_size_2)))
  LOD_y <- mean_bilinear[which(abs(xaxis_orig_2 - LOD_pred) == min(
    abs(xaxis_orig_2 - LOD_pred)))]
  p1 <- p1 + geom_point(data=data.frame(x= LOD_pred, y= y_LOD_pred, 
                                        shape='LOD'),
                        aes_string(x="x", y="y", shape = "shape", guide=FALSE), 
                        colour="purple", size=5)
  LOQ_y <- lower_Q_pred[which(abs(up_noise - lower_Q_pred) == min(
    abs(up_noise - lower_Q_pred)))]
  p1 <- p1 + geom_point(data=data.frame(x= LOQ_pred, y= y_LOQ_pred, 
                                        shape='LOQ'),
                        aes_string(x="x", y="y", shape = "shape", guide=FALSE),
                        colour=orange1,size=5)
  LOD_string <- paste('LOB=', round(LOD_pred, digits=1),sep='')   
  LOQ_string <- paste('LOD=',round(LOQ_pred, digits=1),sep='') 
  p1 <- p1 + guides(colour = guide_legend(order = 1), 
                    linetype = guide_legend(order = 1),
                    shape = guide_legend(order = 2)) + 
    guides(shape=FALSE)
  p1 <- p1 + ggtitle(paste(datain$NAME,'\n', LOD_string, ', ', 
                           LOQ_string,sep="")) + 
    theme(plot.title = element_text(size = 20))
  
  plots_list[[1]] <- p1
  
  if (address != FALSE) {
    print(p1)
    dev.off()
  }
  
  # produce a second plot showing a zoomed view:
  # missing argument for the x limit in the function
  # pick a x limit that is close to the LOD/LOQ:
  if (missing(xlim_plot)) { 
    if (LOQ_pred > 0) {
      xlim <- LOQ_pred * 3.0      
    } else {
      xlim <- unique(Cdata)[4]
    }
  } else {
    xlim <- xlim_plot
  }
  
  if (address != FALSE) {
    allfiles = list.files()
    num = 0
    filenaming = paste0(address, "QuantLim_zoom")
    finalfile = paste0(address, "QuantLim_zoom.pdf")
    while (is.element(finalfile, allfiles)) {
      num = num + 1
      finalfile = paste0(paste(filenaming, num, sep = "-"), ".pdf")
    }
    pdf(finalfile, width = width, height = height)
  }
  
  Idata <- subset(Idata, Cdata < xlim)
  Cdata <- subset(Cdata, Cdata < xlim)
  
  lower_Q_pred <- subset(lower_Q_pred, xaxis_orig_2 < xlim)
  upper_Q_pred <- subset(upper_Q_pred, xaxis_orig_2 < xlim)  
  mean_bilinear <- subset(mean_bilinear, xaxis_orig_2 < xlim)
  xaxis_orig_2 <- subset(xaxis_orig_2, xaxis_orig_2 < xlim)
  
  p1 <- ggplot() + .theme_complete_bw()
  p1 <- p1 + geom_point(data=data.frame(Cdata,Idata) , 
                        aes(x=Cdata,y=Idata), 
                        size=pw*1.5)
  p1 <- p1 + geom_line(data=data.frame(x=xaxis_orig_2,
                                       y=mean_bilinear,
                                       col='mean prediction',
                                       lt = 'mean'),
                       aes_string(x="x", y="y", color="col"), 
                       size = lw) 
  p1 <- p1 + geom_ribbon(data=data.frame(x=xaxis_orig_2,ymin =lower_Q_pred , 
                                         ymax =upper_Q_pred), 
                         aes_string(x="x", ymin="ymin", ymax="ymax"),
                         fill = red1, 
                         alpha = 0.3)
  p1 <- p1 + geom_line(data = data.frame(x=xaxis_orig_2, 
                                         ymin =lower_Q_pred, 
                                         col = 'lower 95% prediction', 
                                         lt = 'Int'), 
                       aes_string(x="x", y="ymin", color = "col"), 
                       size = lw)
  p1 <- p1 + geom_line(data=data.frame(x=xaxis_orig_2, ymax = rep(
    up_noise,length(xaxis_orig_2)),
    col = "95% upper bound of noise"),
    aes_string(x="x", y="ymax", color = "col"),
    size = lw)
  p1 <- p1 + scale_alpha_continuous(guide = 'none') 
  p1 <- p1 + xlab('Spiked Concentration') + ylab('Estimated Concentration')
  p1 <- p1 + theme(axis.text.x = element_text(size = rel(rel_size))) + 
    theme(axis.text.y = element_text(size = rel(rel_size)))
  p1 <- p1 + theme(axis.title.x = element_text(size = rel(rel_size))) + 
    theme(axis.title.y = element_text(size = rel(rel_size)))
  p1 <- p1 + theme(axis.title.y=element_text(vjust=0.7))
  
  p1 <- p1 + scale_color_manual(values = c(blue1,orange1, red1),
                                labels = c(upp_noise,low_pred,
                                           "mean prediction"))
  
  p1 <- p1 + theme(legend.title = element_blank()) + 
    theme(legend.position = c(0.05, 0.5), 
          legend.justification = c(0, 0), 
          legend.text=element_text(size=rel(rel_size_2)))
  LOD_y <- mean_bilinear[which(abs(xaxis_orig_2 - LOD_pred) == min(
    abs(xaxis_orig_2 - LOD_pred)))]
  p1 <- p1 + geom_point(data=data.frame(x=LOD_pred, y=y_LOD_pred, shape='LOD'),
                        aes_string(x="x", y="y", shape = "shape", guide=FALSE),
                        colour="purple",
                        size=5)
  LOQ_y <- lower_Q_pred[which(abs(up_noise - lower_Q_pred) == min(
    abs(up_noise - lower_Q_pred)))]
  p1 <- p1 +  geom_point(data=data.frame(x= LOQ_pred, y= y_LOQ_pred, 
                                         shape='LOQ'),
                         aes_string(x="x", y="y", shape = "shape", guide=FALSE), 
                         colour=orange1,
                         size=5)
  LOD_string <- paste0('LOB=',round(LOD_pred, digits=1))
  LOQ_string <- paste0('LOD=',round(LOQ_pred, digits=1))
  p1 <- p1 + guides(colour = guide_legend(order = 1), 
                    linetype = guide_legend(order = 1),
                    shape = guide_legend(order = 2)) + 
    guides(shape=FALSE)
  p1 <- p1 + ggtitle(paste(datain$NAME,'\n', LOD_string, ', ', 
                           LOQ_string,sep="")) + 
    theme(plot.title = element_text(size = 20))
  
  plots_list[[2]] <- p1
  
  if (address != FALSE) {
    print(p1)
    dev.off()
  }
  
  plots_list
}