R/plot_peptide_sig_tp_proc.R

In mkajano/HDXBoxeR: Analysis of Hydrogen-Deuterium Exchange Mass-Spectrometry Data

#' Preparatory function for showing peptides with significant differences between sets.
#'
#' Returns plot where significantly different peptides are colored in blue-red scheme.
#'
#' @param df average data frame for procent deuteration. Generated using ave_timepoint() function.
#' @param dfup average data frame for deuteration uptake. Generated using ave_timepoint() function.
#' @param pv pvalues dataframes calculated using pv_timepoint() function
#' @param sd standard deviation data.frame generated using sd_timepoint function
#' @param pv_cutoff p-value cutoff here set up to 0.01
#' @param replicates number of replicates in sample. Default set to 3.
#' @param ranges ranges for coloring scheme. Default set to c(-Inf, seq(-30, 30, by=10), Inf)
#' @param nb_row number of peptides in each row. Plotting parameter.
#' @return plot with peptides which are significantly different between sets.
#' @export
peptide_pv_tp_proc<-function(df, dfup, pv, sd,nb_row=100, ranges=c(-Inf, seq(-30, 30, by=10), Inf),
                             pv_cutoff=0.01, replicates=3){
  #preparation significance per residue & coverage
  cl1<-significant_peptide_uptake(dfup, pv, sd, pv_cutoff, replicates)
  cl1[is.na(cl1) ] <- 0


  start_col<-which(colnames(df)=='Start')
  end_col<-which(colnames(df)=='End')
  fc.d<-data.frame((df[,7]-df[,8:dim(df)[2]])/10*cl1)###vector which has significant average
  fc.d[is.nan(fc.d)]<- 0
  fc.d[fc.d == "-Inf"]<- 0
  si.fv<-(fc.d)
  ranges_function(df,si.fv )


  xli=ranges/10; num_ass<-c(-10001:(-10001-(length(xli)-2)))
  for ( i in 1:(length(xli)-1)){
    si.fv[xli[i]< si.fv & si.fv < xli[i+1]] <- num_ass[i] }
  si.fv[si.fv==0]<- (-10000)

  si.fv<-abs(si.fv)-9999

  cbr1<-color_ranges_Blue_Red_heat_map(ranges=xli, c("black"))



  for ( j in 1:dim(si.fv)[2]){
    y1=c(rep(1:nb_row, times=floor(dim(df)[1]/nb_row)), 1:(dim(df)[1]%%nb_row)) ##y values on the plot corresponding to peptides index
    plot(c(1,1), type="n", xlab="", ylab="", lwd=2, col=4, xlim=c(min(df[,start_col]), max(df[,end_col])),
         ylim=c(nb_row, 0), yaxt="n", xaxt="n") ## mock plot, just to have it drawn correct limits set up

    for ( i in 1:dim(df)[1]){
      points(c(df[i,start_col], df[i,end_col]), c(y1[i], y1[i]), type="l",
             col=cbr1[si.fv[i,j]])

    }

    main_nm=str_sub(colnames(df)[j+7], end = -9, start=4)
    mtext(main_nm, side=1, outer=FALSE, line=0, cex=0.6)
    axis(3, at=seq(0, max(df[, end_col])+25, by=25),  tcl=-0.2, labels = F)
    axis(3, at=seq(0, max(df[, end_col])+25, by=50), labels = T, cex.axis=0.65)
    box(lwd=2)
  }}

#' Draws peptides with significant difefrences between sets.
#'
#' Returns plot where significant peptides are colored in blue-red scheme.
#'
#' @param input_proc Dataframe with organized procent deuteration data. Input generated using output_tp_proc() function.
#' @param input_up Dataframe with organized deuteration uptake. Input generated using output_tp() function.
#' @param pv_cutoff p-value cutoff here set up to 0.01
#' @param replicates number of replicates in sample. Default set to 3.
#' @param ranges ranges for coloring scheme. Default set to c(-Inf, seq(-30, 30, by=10), Inf)
#' @param nb_pep_row number of peptides in each row. Plotting parameter. Default set to 100.
#' @return plot with peptides which are significantly different between sets.
#' @examples
#' file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
#' a_up<- output_tp(file_nm)
#' a_proc<- output_tp(file_nm, percent=TRUE)
#' plot_peptide_sig_tp_proc(input_proc=a_proc, input_up=a_up, replicates=3, pv_cutoff=0.01,
#' ranges=c(-Inf,-40, -30,-20,-10, 0,10, 20,30,40, Inf), nb_pep_row=40 )
#' @export
plot_peptide_sig_tp_proc<-function(input_proc, input_up, nb_pep_row=100, ranges=c(-Inf, seq(-30, 30, by=10), Inf),
                                   pv_cutoff=0.01, replicates=3){

  oldpar<-par(no.readonly = TRUE)
  on.exit(par(oldpar))

  pv1<-pv_timepoint(input_up, replicates)
  s1<-sd_timepoint(input_up, replicates)
  av1<-ave_timepoint(input_proc, replicates)
  avu<-ave_timepoint(input_up, replicates)
  par(mar = c(1.5, 1.5, 1.5, 1.5), oma=c(3,2.4,2,2), cex.axis=1,
      cex.main=1, cex.lab=1.1, mgp=c(0.1, 0.4, 0), ps=14, font=2, bg="white", font.lab=2, font.axis=2)
  for ( i in(unique(av1$Deut.Time))){
    message(paste("For time point", i))
    a1=av1[av1$Deut.Time==i,]
    au=avu[avu$Deut.Time==i,]
    p1=pv1[pv1$Deut.Time==i,]
    sd=s1[s1$Deut.Time==i,]
    peptide_pv_tp_proc(a1, au, p1, sd, nb_pep_row,ranges, pv_cutoff, replicates)
    #legend_heat_map_tp(av1)
    mtext(i, side=4, outer=FALSE, line=0.2, cex=0.7)}
  mtext(c("Residues"),  c(NORTH<-3),line=0, outer=TRUE, cex=0.7)}

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