R/flowDensity_methods.R

In mehrnoushmalek/flowDensity: Sequential Flow Cytometry Data Gating

setMethod(f="flowDensity",
          signature=c("flowFrame", "ANY", "logical"),
          definition=function(obj, channels, position, ...){
            .deGate2D(f=obj, channels=channels, position=position, ...)
          }
)

setMethod(f="flowDensity",
          signature=c("CellPopulation", "ANY", "logical"),
          definition=function(obj, channels, position, ...){
            .deGate2D(f=obj@flow.frame, channels=channels, position=position, ...)
          }
)


setMethod(f="getflowFrame",
          signature=c("CellPopulation"),
          definition=function(obj){
            .getDataNoNA(obj)
          }
)

setMethod(f="plot", signature=c("flowFrame", "CellPopulation"),
          definition=function(x, y, ...){
            .deGatePlot(f=x, cell.population=y, ...)
          }
)


deGate <- function(obj,channel, n.sd = 1.5, use.percentile = FALSE,  percentile =NA,use.upper=FALSE, upper = NA,verbose=TRUE,twin.factor=.98,
                   bimodal=F,after.peak=NA,alpha = 0.1, sd.threshold = FALSE, all.cuts = FALSE,
                   tinypeak.removal=1/25, adjust.dens = 1,count.lim=20,magnitude=.3,slope.w=4,seq.w=4,spar=.4, ...){
  
  ##========================================================================================================================================
  ## 1D density gating method
  ## Args:
  ##   obj: a 'FlowFrame' object, 'CellPopulation'
  ##   channel: a channel's name or an integer to specify the channel
  ##   n.sd: an integer that is multiplied to the standard deviation to determine the place of threshold if 'sd.threshold' is 'TRUE'
  ##   use.percentile: if TRUE, forces to return the 'percentile'th threshold
  ##   percentile: a value in [0,1] that is used as the percentile. the default is NA. If set to a value(n) and use.percentile=F, it returns the n-th percentile, for 1-peak populations. 
  ##   use.upper: if TRUE, forces to return the inflection point based on the first (last) peak if upper=F (upper=T)
  ##   upper: if TRUE, finds the change in the slope at the tail of the density curve, if FALSE, finds it at the head.
  ##   verbose: When FALSE doesn't print any messages
  ##   twin.factor: reverse of tinypeak.removal for ignoring twinpeaks
  ##   bimodal: if TRUE, splits population closer to 50-50 when there are more than 2 peaks
  ##   after.peak: if TRUE (FALSE) and bimodal=FALSE, it chooses a cuttoff after(before) the maximum peak.
  ##   alpha: a value in [0,1) specifying the significance of change in the slope which would be detected
  ##   sd.threshold: if TRUE, it uses 'n.sd' times standard deviation for gating
  ##   all.cuts: if TRUE, it returns all the cutoff points whose length+1 can roughly estimate the number of cell subsets in that dimension
  ##   tiny.peak.removal: a values in [0,1] for ignoring tiny peaks in density. Default is 1/25
  ##   adjust.dens: The smoothness of density, it is same as adjust in density(.). The default value is 1 and should not be changed unless necessary
  ##   count.lim: minimum limit for event count in order to calculate the threshold. Default is 20
  ##   magnitude: Value between (0,1) for finding changes in the slope that are smaller than max(peak)*magnitude
  ##   slope.w: Value used for tracking slope. It's a value from 1 to length(density$y), that skip looking at all data points, and instead look at point(i) and point(i+w)
  ##   seq.w: value used for making the sequence of density points, used in trackSlope
  ##   spar: value used in smooth.spline function, used in generating the density, default is .4
  ## Value:
  ##   cutoffs, i.e. thresholds on the 1D data
  ## Author:
  ##   M. Jafar Taghiyar & Mehrnoush Malek
  ##-----------------------------------------------------------------------------------------------------------------------------------------
  
  
 
  if (class(obj)=="CellPopulation")
    obj<-.getDataNoNA(obj)
  .densityGating(obj, channel, n.sd = n.sd, use.percentile = use.percentile, percentile = percentile, use.upper=use.upper,upper = upper,verbose=verbose,
                 twin.factor=twin.factor,bimodal=bimodal,after.peak=after.peak,alpha = alpha, sd.threshold = sd.threshold,all.cuts = all.cuts,
                 tinypeak.removal=tinypeak.removal, adjust.dens=adjust.dens,count.lim=count.lim,magnitude=magnitude,slope.w=slope.w,
                 seq.w=seq.w,spar=spar, ...)
}

getPeaks <-  function(obj, channel,tinypeak.removal=1/25, adjust.dens=1,verbose=F,twin.factor=1,spar=.4,...){
  ##===================================================
  #Finding peaks for flowFrame objects or numeric vectors
  ##==================================================
  if(class(obj)=="numeric"|class(obj)=="vector"){
    x<-obj
    channel <-NA
  }else if (class(obj)=="CellPopulation")
  { 
    obj<-.getDataNoNA(obj)
    x <- exprs(obj)[, channel]
  }else if (class(obj)=="flowFrame"){
    x <- exprs(obj)[, channel]
  }
  if ( class(obj)=="density")
  { 
    dens <- obj 
  }else{
    n<- which(!is.na(x))
    if (length(n)< 3)
    {
      if(verbose)
        cat("Less than 3 cells, returning NA as a Peak.","\n")
      return(list(Peaks=NA, P.ind=0,P.h=0))
    }
    dens <- .densityForPlot(data = x, adjust.dens=adjust.dens,spar=spar,...)
  }
  
  all.peaks <- .getPeaks(dens, peak.removal=tinypeak.removal,twin.factor=twin.factor)
  return(all.peaks)
}

plotDens <- function(obj, channels,col, main, xlab, ylab, xlim,ylim, pch=".", 
                     density.overlay=c(FALSE,FALSE),count.lim=20, dens.col=c("grey48","grey48"),cex=1,
dens.type=c("l","l"),transparency=1, adjust.dens=1,show.contour=F, contour.col="darkgrey",verbose=TRUE, ...){
  
  ##===================================================
  ## Plot flowCytometry data with density-based color
  ##---------------------------------------------------
  
  if (class(obj)=="CellPopulation")
    obj <- obj@flow.frame
    flow.frame<-obj
  f.exprs <- exprs(flow.frame)
  na.ind <-which(is.na(f.exprs[,1]))
  if(length(na.ind)>0)
    f.exprs<-f.exprs[-na.ind,]
  f.data <- pData(parameters(flow.frame))
  f.col.names <- colnames(flow.frame)
  if (length(f.exprs[,channels[1]])<count.lim)
  {
    col<-1
    pch<-20
  }else if(missing(col)){
    colPalette <- colorRampPalette(c("blue", "turquoise", "green", "yellow", "orange", "red"))
    col <- densCols(f.exprs[,channels], colramp = colPalette)
  }
  if(is.numeric(channels))
    channels <- f.col.names[channels]
  if(missing(xlab))
    xlab <- paste("<", channels[1], ">:", f.data$desc[which(f.col.names==channels[1])], sep = "")
  if(missing(ylab))
    ylab <- paste("<", channels[2], ">:", f.data$desc[which(f.col.names==channels[2])], sep = "")
  if(missing(main))
    main <- "All Events"
  
  if (nrow(flow.frame)<1)
  {

    plot(1, type="n",main = paste0(main,ifelse(verbose,yes=": 0 cells",no="")), axes=F, ylab=ylab, xlab=xlab,...)
  }else if (nrow(flow.frame)==1)
  {
      if (missing(xlim))
    xlim <- c(f.exprs[,channels[1]]*.6,f.exprs[,channels[1]]*1.5)
  if (missing(ylim))
    ylim <- c(f.exprs[,channels[2]]*.6,f.exprs[,channels[2]]*1.5)
    graphics::plot(x=f.exprs[,channels[1]],y=f.exprs[,channels[2]], col = col, 
                   pch = 19,  main = main, xlab =xlab,cex=cex,ylab = ylab,xlim=xlim,ylim=ylim, ...)  
    
  }else{
     if (missing(xlim))
    xlim <- range(f.exprs[,channels[1]],na.rm = T)
  if (missing(ylim))
    ylim <- range(f.exprs[,channels[2]],na.rm = T)
    
    if (!any(density.overlay) )
    {
      
      graphics::plot(f.exprs[,channels], col = col, pch = pch, main = main, 
                     xlab =xlab,cex=cex,ylab = ylab,xlim=xlim,ylim=ylim, ...)
      if (show.contour)
      {
        flowViz::contour(x=flow.frame,y = channels, add=TRUE,col=contour.col,lty=1,lwd=1.5)
      }
      
    }else if (density.overlay[1] & density.overlay[2])
      {
        x.dens <- density(f.exprs[,channels[1]],adjust=adjust.dens)
        graphics::plot(x.dens$x, x.dens$y,main =main,cex=2.2,col=dens.col[1], type= dens.type[1],pch=".",yaxt="n",xlim=xlim,
                       xlab=xlab,ylab=ylab,...)
        par(new=T)
      
        y.dens <- density(f.exprs[,channels[2]],adjust=adjust.dens)
        graphics::plot(y.dens$y,y.dens$x,main ="",cex=2.2,col=dens.col[2],type= dens.type[2], pch=".",
                       ylab="",xlab="",xaxt="n", ylim=ylim,...)
        par(new=T)
        col<-adjustcolor(col,alpha.f = transparency)
        graphics::plot(f.exprs[,channels], col = col, pch = pch,axes=F,xlab="",ylab="",main = "",cex=cex,ylim=ylim,xlim=xlim)
      
      }else if (density.overlay[1] & !density.overlay[2])
      {
        x.dens <- density(f.exprs[,channels[1]],adjust=adjust.dens)
        graphics::plot(x.dens$x, x.dens$y,main =main,cex=2.2,col=dens.col[1], type= dens.type[1],pch=".",yaxt="n",xlim=xlim,
                       xlab=xlab,ylab=ylab)
        par(new=T)
        
        graphics::plot(1,main ="",cex=1,col="white",type= dens.type[2], pch=".",
                       ylab="",xlab="",xaxt="n", ylim=ylim)
        par(new=T)
        col<-adjustcolor(col,alpha.f = transparency)
        graphics::plot(f.exprs[,channels], col = col, pch = pch,axes=F,xlab="",ylab="",main = "",cex=cex,ylim=ylim,xlim=xlim)
        
      }else{
        graphics::plot(1,main ="",col="white", type= dens.type[1],pch=".",yaxt="n",xlim=xlim,
                       xlab=xlab,ylab=ylab,...)
        par(new=T)
        y.dens <- density(f.exprs[,channels[2]],adjust=adjust.dens)
        graphics::plot(y.dens$y,y.dens$x,main ="",cex=2.2,col=dens.col[2],type= dens.type[2], pch=".",
                       ylab="",xlab="",xaxt="n", ylim=ylim,...)
        par(new=T)
        col<-adjustcolor(col,alpha.f = transparency)
        graphics::plot(f.exprs[,channels], col = col, pch = pch,axes=F,xlab="",ylab="",main = "",cex=cex,ylim=ylim,xlim=xlim)
      }
      
  }
}
  
  
  
  notSubFrame <- function(obj, channels, position = NA, gates, filter){
    
    ##===============================================================
    ## Excludes the subframe gated by 'gates' form given 'flow.frame'
    ##---------------------------------------------------------------
    if (class(obj)=="CellPopulation")
      flow.frame <- obj@flow.frame
    else if (class(obj)=="flowFrame")
      flow.frame <- obj
    else
      stop("Input should be either a cellPopulation or flowFrame.")
    if(missing(filter))
      .notSubFrame(flow.frame=flow.frame, channels=channels, position=position, gates=gates)
    else
      .notSubFrame(flow.frame=flow.frame, channels=channels, filter=filter)
  }
  
  nmRemove <- function(flow.frame, channels, neg=FALSE, verbose=FALSE,return.ind=FALSE){
    ##===============================
    ## Removes negatives and margines and replaces them with NA
    ##-------------------------------
    
    inds<-vector("list",length(channels))
    names(inds)<-channels
    new.f <- flow.frame
    for(chan in channels){
      j<- ifelse(is.character(chan),yes= which(colnames(flow.frame)==chan),no=chan)
      if(length(j)==0)
        next
      d <- exprs(new.f)[,j]
      m <- new.f@parameters@data$maxRange[j]
      if(neg)
        i <- which(d > 0 & d != m)
      else
        i <- which(d != m)
      exprs(new.f)[i, ] <- exprs(new.f)[i,]
      if (verbose)
        cat(length(which(!is.na(d)))-length(i), "Margin events removed from",colnames(new.f)[j],"\n")
      exprs(new.f)[-i,] <- NA
      inds[[chan]]<-which( is.na(exprs(new.f)[,1]))
    }
    if(return.ind)
      return(inds)
    exprs(new.f) <- exprs(new.f)[which(!is.na(exprs(new.f))[,1]),]
    return(new.f)
  }