R/visualization.R

Defines functions plotMGSQuality computeSignalMetrics abondanceScore presenceScore plotCors2 plotCors plotPvals plotBarcodeBW plotBarcode2 plotBarcode

Documented in abondanceScore computeSignalMetrics plotBarcode plotBarcode2 plotBarcodeBW plotCors plotCors2 plotMGSQuality plotPvals presenceScore

#' \code{plotBarcode} 
#' @title plotBarcode
#' @description plots the intensity of a frequency matrix with a 4-fold color step
#' @author Edi Prifti & Emmanuelle Le Chatelier
#' @param data : a frequency matrix to be visualized
#' @param main : the main title of the plot empty by default
#' @return nothing
#' @note this may be slightly affected by the size of the catalogue when comparing different studies
plotBarcode <- function(data, main=""){
  cols <- list()
  cols$val <- c(0, 0, 0.0000001, 0.0000004, 0.0000016, 0.0000064, 0.0000256, 0.0001024, 0.0004096, 0.0016384)
  cols$col <- c("white", "deepskyblue", "blue", "green3", "yellow", "orange", "red", "orangered2", "darkred")
  #cols$col <- c("white", "skyblue", "deepskyblue3", "green3", "yellow", "orange", "red", "darkred", "black")
  image(t(data[nrow(data):1,]), breaks=cols$val, col=cols$col, axes=FALSE, main=main)
  box()
}


#' \code{plotBarcode2} 
#' @title plotBarcode2
#' @description plots the intensity of a frequency matrix with a 4-fold color step. Usually used
#'      for complex figures where different MGS are overlapped and annotated with different data.
#' @author Edi Prifti & Emmanuelle Le Chatelier
#' @param data : a frequency matrix to be visualized
#' @param main : the main title of the plot empty by default
#' @param ylabl : label for the left y axis
#' @param ylabr : label for the right y axis
#' @param col.axisl : color for the left y axis
#' @param col.axisr : color for the right y axis
#' @param box : default FALSE.
#' @return nothing
#' @note this may be slightly affected by the size of the catalogue when comparing different studies
plotBarcode2 <- function(data, main="",ylabl="", ylabr="", col.axisl="white", col.axisr="white", box=FALSE){
  #par(mai = par()$mai-c(0,1,0,0))
  cols <- list()
  cols$val <- c(0, 0, 0.0000001, 0.0000004, 0.0000016, 0.0000064, 0.0000256, 0.0001024, 0.0004096, 0.0016384)
  cols$col <- c("white", "deepskyblue", "blue", "green3", "yellow", "orange", "red", "orangered2", "darkred")
  image(t(data[nrow(data):1,]), breaks=cols$val, col=cols$col, axes=FALSE, main=main)
  axis(2,at=0.5, labels=ylabl, las=1, cex.axis=2, col.axis = col.axisl, col.ticks="white")
  axis(4,at=0.5, labels=ylabr, las=1, cex.axis=2, col.axis = col.axisr, col.ticks="white")
  if(box){
    #par(mai = par()$mai+c(0,1,0,0))
    box(col="darkgray",cex=0.5, which="plot")
  }
}

#' \code{plotBarcodeBW} 
#' @title plotBarcodeBW
#' @description plots in black when a signal is different from zero and white otherwise
#' @author Edi Prifti
#' @param data : a frequency matrix to be visualized
#' @param main : the main title of the plot empty by default
#' @return nothing
plotBarcodeBW <- function(data, main=""){
  cols <- list()
  cols$val <- c(0, 0, 1)
  cols$col <- c("white", "black")
  image(t(data[nrow(data):1,]), breaks=cols$val, col=cols$col, axes=F, main=main)
  box()
}


#' \code{plotPvals} 
#' @title plotPvals
#' @description plots a heatmap of a matrix composed of p-values. Gray is not significant at p=0.05
#'      and significance decrases from skyblue to darkred
#' @author Edi Prifti
#' @param data : a frequency matrix to be visualized
#' @param main : the main title of the plot empty by default
#' @return nothing
plotPvals <- function(data, main=""){
  cols <- list()
  cols$val <- c(0, 0, 1e-20, 1e-8, 1e-7, 1e-6, 1e-5, 1e-4, 1e-3, 5e-2,1)
  cols$col <- c("white", "skyblue", "deepskyblue3", "green3", "yellow", "orange", "red", "darkred", "black","gray")
  image(t(data[nrow(data):1,]), breaks=cols$val, col=cols$col, axes=F, main=main)
  box()
}

#' \code{plotCors} 
#' @title plotCors
#' @description plots a heatmap of a matrix composed of correlation values from -1 to 1. The blue 
#'      colors are negative correlations while the red are positive
#' @author Edi Prifti
#' @param data : a frequency matrix to be visualized
#' @param main : the main title of the plot empty by default
#' @return nothing
plotCors <- function(data, main=""){
  cols <- list()
  cols$val <- c(-1, -1, -0.75, -0.5, -0.25, 0, 0.25, 0.5, 0.75, 1, 1)
  cols$col <- c("darkblue", "blue3", "blue2", "lightblue", "white", "white", "orange","red3" ,"darkred", "black")
  image(t(data[nrow(data):1,]), breaks=cols$val, col=cols$col, axes=F, main=main)
  box()
}

#' \code{plotCors2} 
#' @title plotCors2
#' @description similar to plotCors but with more levels of colors
#' @author Edi Prifti
#' @param data : a frequency matrix to be visualized
#' @param main : the main title of the plot empty by default
#' @return nothing
plotCors2 <- function(data, main=""){
  col <- list()
  col$val <- seq(-1,1,1e-1)
  col$col <- gplots::colorpanel(20,low="blue",mid="white",high="red")
  image(t(data[nrow(data):1,]), breaks=col$val, col=col$col, axes=F, main=main); 
  box(); grid(nx=ncol(data),ny=nrow(data),col="black")
  axis(1,labels=colnames(data), at=(((1:ncol(data))-1)/ncol(data))*1.165,las=2)
  axis(2,labels=rownames(data), at=(((1:nrow(data))-1)/nrow(data))*1.17,las=2)
}


#===========================================================================================
# This section computes and visualizes scores of data variation within a given MGS cluster.
# Some of the scores were inspired from # http://en.wikipedia.org/wiki/Coefficient_of_variation
#===========================================================================================

#' \code{presenceScore} 
#' @title presenceScore
#' @description Computes the percentage [0,1]of values of a vector that are aboove a given threshold
#' @author Edi Prifti
#' @param vect : a numerical vector
#' @param th : the threshold to be applied, default is 0
#' @return percentage
presenceScore <- function(vect, th=0){
  if(!is.numeric(vect) & !is.integer(vect)) warning("Please provide a numerical vector.")
  return(sum((vect>th)+0.0)/length(vect))
}

#' \code{abondanceScore} 
#' @title abondanceScore
#' @description Computes the sum of the vectors divided by the prevalence
#' @author Edi Prifti
#' @param vect : a numerical vector
#' @param th : the threshold to be applied, default is 0
#' @return a mean abundance 
abondanceScore <- function(vect, th=0){
  return(sum(vect) / sum((vect>th)+0.0))
}

#' \code{computeSignalMetrics} 
#' @title computeSignalMetrics
#' @description Computes scores of data variation within a given MGS cluster.
#' @author Edi Prifti
#' @param dat : a matrix where operations will be performed on the columns. Please transpose if operations
#' are needed in the rows.
#' @return a matrix of results where scores are in the columns 
computeSignalMetrics <- function(dat){
  # elements for the scores
  moy<- apply(dat, 2, mean)
  std <- apply(dat, 2, sd)
  var <- apply(dat, 2, var)
  q1 <- apply(dat, 2, quantile, 0.25)
  q2 <- apply(dat, 2, quantile, 0.5)
  q3 <- apply(dat, 2, quantile, 0.75)
  
  # Variance to mean ratio
  variance_to_mean <- (var/moy)
  variance_to_mean[is.nan(variance_to_mean)] <- 0
  
  # signal to noise ratio
  signal_to_noise <- (moy/std)
  signal_to_noise[is.nan(signal_to_noise)] <- 0
  
  # Coefficient of variation
  variation_coefficient <- (std/moy)
  variation_coefficient[is.nan(variation_coefficient)] <- 0
  
  # Effiency
  efficiency <- (std^2/moy^2)
  efficiency[is.nan(efficiency)] <- 0
  
  # Quartile coefficient of dispersion
  quartile_dispertion <- (q3-q1)/q2
  quartile_dispertion[is.nan(quartile_dispertion) | is.infinite(quartile_dispertion)] <- 0
  
  res <- as.data.frame(cbind(variance_to_mean, signal_to_noise, variation_coefficient, efficiency, quartile_dispertion))
  return(res)
}


#' \code{plotMGSQuality} 
#' @title plotMGSQuality
#' @description Visualized scores of data variation within a given MGS cluster as well as the barcode of the MGS.
#' A subset of 50 most connected genes is also plotted the same way.
#' @author Edi Prifti
#' @param dat : a matrix where operations will be performed on the columns. Please transpose if operations
#' are needed in the rows. This is typically an MGS frequency matrix with genes in the rows.
#' @param main : the name of the plot
#' @param scores : weather the function should return the computed scores or not. By default is TRUE.
#' @return a matrix of results where scores are in the columns 
plotMGSQuality <-function(dat, main="mgs", scores=TRUE){
  par(mfcol=c(6,2), xaxs='i',yaxs='i',mar=c(2,2,2,1))
  colpan <- c("black", gplots::colorpanel(n=20,low="darkblue",mid="darkorchid",high="red"))
  size <- 50
  if(nrow(dat) < size) {
    size <- nrow(dat)
    warning("need more than 50 genes")
  }
  dat50 <- dat[1:size,]
  plotBarcode(dat50, main=paste(main, nrow(dat50),"genes"))
  scores50 <- computeSignalMetrics(dat50)
  for(i in 1:ncol(scores50)){
    if(all(scores50[,i]==0)){
      cols="black"
    }else{
      cols <- colpan[as.numeric(cut(scores50[,i], breaks = 20))]
    }
    plot(scores50[,i], pch=19,cex=0.4, main=colnames(scores50)[i], ylab="", xlab="individual index",col=cols)
  }
  
  plotBarcode(dat, main=paste(main, nrow(dat),"genes"))
  scores <- computeSignalMetrics(dat)
  for(i in 1:ncol(scores)){
    if(all(scores[,i]==0)){
      cols="black"
    }else{
      cols <- colpan[as.numeric(cut(scores[,i], breaks = 20))]
    }
    plot(scores[,i], pch=19,cex=0.4, main=colnames(scores)[i], ylab="", xlab="individual index",col=cols)
  }
  if(scores) return(scores)
}

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momr documentation built on May 29, 2017, 5:47 p.m.