R/beads.R

In flowBeads: flowBeads: Analysis of flow bead data

gateScatter <- function(bead.data, verbose=T, ...) {
  #gating on scatter is important to eliminate doublets
  #remove all negative SSC and FSC
  if (verbose) cat('Removing all negative FSC and SSC...\n')
  bead.data <- bead.data[ bead.data@exprs[,'FSC'] > 0 & bead.data@exprs[,'SSC'] > 0, ]
  #gate on FSC SSC first to retrieve singlets
  if (verbose) cat('Gating on FSC and SSC with norm2Filter...\n')
  bead.data <- Subset(bead.data, filter(bead.data, flowCore::norm2Filter(filterId="ScatterFilter", x=c("FSC", "SSC"), scale.factor=1)))
  return(bead.data)
}


#' gateBeads 
#' 
#' @description
#' \code{gateBeads} gates on all channels, apply scatter gate first.
#' Find parameters in MEF data.frame which are also present in BeadFlowFrame
#' The number of expected bead populations is by default six and it is assumed that
#' that there is the same number of beads in each population.
#' @param bead.data The BeadFlowFrame object to gate.
#' @param K The number of bead populations expected.
#' @param verbose Whether to print debug information.
#' @return \linkS4class{GatedBeadFlowFrame}
#' @rdname gateBeads-methods
#' @export
#' @docType methods
#' @examples
#' data(beads1)
#' gateBeads(beads1)
setMethod('gateBeads',
          signature=signature(bead.data='BeadFlowFrame'),
          definition=function(bead.data, K=NULL, verbose=F, ...) {
            #bead.data <- gateScatter(bead.data, verbose, ...)
            #print(bead.data)
              if (is.null(K)) K <- dim(bead.data@beads.mef)[1]
            all.parameters <- dimnames(bead.data@exprs)[[2]]
            bead.parameters <- grep('^(SSC|FSC)', all.parameters, value=T, invert=T)
            gated.bead.data <- as(bead.data, 'GatedBeadFlowFrame')
            #gating.parameter <- all.parameters[grep(gating.parameter, all.parameters, ignore.case=T)]
            #for all parameters except forward and side scatter
            #parameters which overlap between MEF file and bead.data
            trans <- bead.data@trans
            inv.trans <- bead.data@inv.trans
            params <- getParams(bead.data)
            x <- bead.data@exprs[,params]
            labels <- apply( x, 2, function(y) {
                                        y <- trans(y)
                                        breaks <- round(quantile(sort(y), probs=seq(0,1,1/K)), digits=3)
                                        #print(breaks)
                                        if (length(unique(breaks)) != length(breaks)) return(NA)
                                        levels <- cut(sort(y), breaks=breaks)
                                        #print(table(levels))
                                        centers <- as.numeric(tapply(sort(y), levels, mean))
                                        #print(centers)
                                        res <- kmeans(y, centers) 
                                        #print(table(res$cluster))
                                        return(res$cluster)
                                        } )
            labels <- round(apply(data.frame(labels), 1, function(x) median(x, na.rm=T)))
            #if (verbose) cat('Number of clusters estimated', K, '\n')
            #print(table(labels))
            stats.fun <- c('count', 'mean.fi', 'sd.fi', 'cv')
            gated.bead.data@clustering.stats <- array(dim=c(length(stats.fun),length(bead.parameters),K), dimnames=list(stats.fun,bead.parameters))           
            #sort the labels
            labels <- factor(labels, levels=names(sort(tapply(bead.data@exprs[,params[1]], labels, min))))
            levels(labels) <- 1:K
            gated.bead.data@labels <- labels
            for (p in params) {
              x <- bead.data@exprs[,p]
              gated.bead.data@clustering.stats['count',p,] <- as.numeric(tapply(x,labels, length))
              gated.bead.data@clustering.stats['mean.fi',p,] <- as.numeric(tapply(x,labels, mean))
              gated.bead.data@clustering.stats['sd.fi',p,] <- as.numeric(tapply(x,labels, sd))
              gated.bead.data@clustering.stats['cv',p,] <- as.numeric(100*as.numeric(tapply(x,labels, sd))/as.numeric(tapply(x,labels, mean)))
            }
            #we can compute the MEF transform for these
            common.params <- intersect(bead.parameters, getMEFparams(bead.data))
            if (verbose) cat('Common bead fluorochromes and channels:', common.params, '\n')
            for (cp in common.params) {
                mef <- bead.data@beads.mef[-1,cp]
                mfi <- gated.bead.data@clustering.stats['mean.fi',cp,]
                m <- lm( trans(mef) ~ trans(mfi[-1]) )
                a <- as.numeric(round(m$coefficients[1], digits=3))
                b <- as.numeric(round(m$coefficients[2], digits=3))
                rse <- as.numeric(round(summary(m)$sigma, digits=3))
                f <- function(a,b) {force(a); force(b); function(x) inv.trans(b*trans(x)+a)}
                gated.bead.data@mef.transform[[cp]] <- list(alpha=a, beta=b, m=m, rse=rse, fun=f(a,b))
            }
            return(gated.bead.data)
          } 
)




#' toMEF
#' 
#' @description
#' Given bead.data and a flow.data apply the MEF transform to matching channels in \code{flow.data}.
#' @param bead.data The GatedBeadFlowFrame object containing the MEF transform.
#' @param flow.data The flowFrame object on which to apply the transform.
#' @rdname toMEF-methods
#' @export
#' @docType methods
setMethod('toMEF',
          signature=signature(bead.data='GatedBeadFlowFrame', flow.data='flowFrame'),
          definition=function(bead.data, flow.data) {
            #standardise file name
            colnames(flow.data@exprs) <- gsub('.A$','', toupper(colnames(flow.data@exprs)))
            flow.data@parameters@data['name'] <- colnames(flow.data@exprs)
            for (p in names(bead.data@mef.transform)) {
              normalise <- bead.data@mef.transform[[p]]$fun
              x <- flow.data@exprs[,p]
              flow.data@exprs[,p] <- normalise(x)
            }
            return(flow.data)
          }
)


#' absoluteNormalise
#' 
#' @description
#' Absolute normalise to align peaks of bead.data to MEF.
#' @return A list of affine functions from transformed MFI relative coordinates to transformed MEF absolute coordinates.
#' @param \code{bead.data} \code{\linkS4class{GatedBeadFlowFrame}}
#' @param mef.data \linkS4class{data.frame}
#' @rdname absoluteNormalise-methods
#' @export
#' @docType methods
setMethod('absoluteNormalise',
          signature=signature(bead.data='GatedBeadFlowFrame', mef.data='data.frame'),
          definition=function(bead.data, mef.data) {
            params <- intersect(getParams(bead.data), names(mef.data))
            absolute.normalise <- list()
            for (p in params) {
              mfi <- bead.data@trans(bead.data@clustering.stats['mean.fi', p,])
              mef <- bead.data@trans(mef.data[,p])
              m <- lm( mef ~ mfi[-1] )
              a <- as.numeric(round(m$coefficients[1], digits=3))
              b <- as.numeric(round(m$coefficients[2], digits=3))
              rse <- as.numeric(round(summary(m)$sigma, digits=3))
              f <- function(a, b) {force(a); force(b); function(x) b*x+a}
              absolute.normalise[[p]] <- list( mfi=mfi, mef=mef, alpha=a, beta=b, m=m, rse=rse, fun=f(a,b) )
            }
            return(absolute.normalise)
          }
)

#' relativeNormalise
#' 
#' @description
#' Relative normalise to align peaks of bead.data1 to those of bead.data2
#' Returns a list of affine functions from transformed MFI day one coordinates to transformed MFI day two coordinates.
#' This permits comparison of channels across two days, provided the detector is stable, even in the absence of absolute MEF values.
#' @return A list of affine functions from MFI day one coordinates to MFI day two coordinates.
#' @param bead.data1: \code{\linkS4class{GatedBeadFlowFrame}} object with MFIs from day one
#' @param bead.data2: \code{\linkS4class{GatedBeadFlowFrame}} object with MFIIs from day two
#' @rdname relativeNormalise-methods
#' @export
#' @docType methods
setMethod('relativeNormalise',
          signature=signature(bead.data1='GatedBeadFlowFrame', bead.data2='GatedBeadFlowFrame'),
          definition=function(bead.data1, bead.data2) {
            params <- intersect(getParams(bead.data1), getParams(bead.data2))
            relative.normalise <- list()
            for (p in params) {
              mfi1 <- bead.data1@trans(bead.data1@clustering.stats['mean.fi', p,])
              mfi2 <- bead.data2@trans(bead.data2@clustering.stats['mean.fi', p,])
              m <- lm( mfi2 ~ mfi1 )
              a <- as.numeric(round(m$coefficients[1], digits=3))
              b <- as.numeric(round(m$coefficients[2], digits=3))
              rse <- as.numeric(round(summary(m)$sigma, digits=3))
              f <- function(a, b) {force(a); force(b); function(x) b*x+a}
              relative.normalise[[p]] <- list( mfi1=mfi1, mfi2=mfi2, alpha=a, beta=b, m=m, rse=rse, fun=f(a,b))
            }
            return(relative.normalise)
          }
)


#' generateReport
#' 
#' Generate an HTML report from a Markdown template using \link{knitr}.
#' @seealso knitr
#' @param bead.data \code{\linkS4class{GatedBeadFlowFrame}}
#' @param output.file name of the file to which to output the HTML report.
#' @rdname generateReport-methods
#' @export
#' @docType methods
setMethod('generateReport',
          signature=signature(bead.data='GatedBeadFlowFrame', output.file='character'),
          definition=function(bead.data, output.file, template=system.file("markdownTemplates/bead-report.Rmd", package = "flowBeads")) {
            #knitr::knit2html(template, options=c("use_xhtml","smartypants","mathjax","highlight_code"), out=output.file)
            knitr::knit2html(template, out=output.file)
          }
)