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fcs.R
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fcs.R
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(require(sp))
(require(cluster))
(require(KernSmooth))
(require(iterators))
#suppressPackageStartupMessages(require(scales))
(require(ggplot2))
(require(flowCore))
(require(lubridate))
(require(GGally))
(require(ellipse))
(require(mvtnorm))
(require(tools))
(require(mixtools))
(require(R.utils))
(require(flowClust))
applyTransforms <- function(x, transforms) {
n <- 1
transforms <- transforms[colnames(x)]
apply(x, 2, function(y) {
y <- transforms[[n]](y)
n <<- n+1
return(y)
})
}
trans <- function(x, m=4.5) {
cbind(x[,grep('^SSC|^FSC',colnames(x))], apply(x[,grep('^SSC|^FSC',colnames(x),invert=TRUE)], 2,
function(y) {
r <- log10(max(y)/abs(min(y)))
w <- (m-r)/2
logicleTransform(w=)(y)
}))
}
# Putting scatter on the same scale as fluorescence,
# since recursive partitioning uses variance and range to
# select markers.
#if (RECT) rect(xleft=min(X[,1]),ybottom=min(X[,2]),xright=max(X[,1]),ytop=max(X[,2]),lwd=2)
transform.scatter <- function(fcs.data) {
trans <- function(x) 10*(x-min(x))/(max(x)-min(x))
fcs.data[,'FSCA'] <- trans(fcs.data[,'FSCA'])
fcs.data[,'SSCA'] <- trans(fcs.data[,'SSCA'])
return(fcs.data)
}
colMedians <- function(x) apply(x,2,median)
colMin <- function(x) apply(x,2,min)
colMax <- function(x) apply(x,2,max)
colQuantile <- function(x,prob) apply(x,2,quantile,prob)
###
plot.normalised.density <- function(d, ...) {
class(d) <- 'density'
graphics::plot(d, ...)
}
###
normalised.density <- function(x, ...) {
d <- density(x, ...)
d$y <- d$y/sum(d$y)
class(d) <- 'normalised.density'
return(d)
}
# flowClust::box
box <- function (data, lambda) {
if (length(lambda) > 1 || lambda == 1) return(data)
else if (length(lambda) > 1 || lambda != 0) return((sign(data) * abs(data)^lambda - 1)/lambda)
else if (is.na(lambda) || is.null(lambda)) return(data)
else return(log(data))
}
#reassignInPackage('box', pkgName='flowClust', rbox)
# to perform reverse box-cox transformation (multivariate)
# flowClust::rbox
rbox <- function (data, lambda)
{
if (length(lambda) > 1 || lambda == 1) return(data)
else if (length(lambda) > 1 || lambda != 0) return(sign(lambda * data + 1) * (sign(lambda * data + 1) * (lambda * data + 1))^(1/lambda))
else if (is.na(lambda) || is.null(lambda)) return(data)
else return(exp(data))
}
#reassignInPackage('rbox', pkgName='flowClust', rbox)
flowclust <- function(d, K, B=500, level=.9, u.cutoff=.5, post.threshold=.99)
flowClust::flowClust(d,K=K,B=B,level=level,u.cutoff=u.cutoff,trans=0,lambda=1,control=list(B.lambda=0))
mixclust <- function(d,G,sub) mclust::Mclust(d,G=G,initialization=list(subset=sub), modelNames=c('VVV'))
prior.flowClust <- function(d, prior.res, B=500, level=.9, u.cutoff=.5, post.threshold=.99, kappa=1) {
prior <- flowClust2Prior(prior.res,kappa=kappa)
return(flowClust::flowClust(d,K=prior.res@K,B=B,usePrior='yes',prior=prior,level=level,u.cutoff=u.cutoff,trans=0,lambda=1,control=list(B.lambda=0)))
}
# findInterval returns -1 if before; 1 if within range; 2 if greater
in.range <- function(x, interval, all.inside=TRUE, ...) findInterval(x, interval, all.inside, ...)==1
#`%in%` <- function(x, r) in.range(x,r)
#
percentile.filter <- function(fcs.data, bottom=.01, top=.99) {
f <- function(x) in.range(x, quantile(x, probs=c(bottom,top)))
if(is.null(ncol(fcs.data)))
f(fcs.data)
else
apply( apply( fcs.data, 2, f), 1, all )
}
fcs3.filter <- function(fcs) {
#remove negative scatter
fcs <- fcs[fcs@exprs[,'SSC-A']>1,]
fcs <- fcs[fcs@exprs[,'FSC-A']>1,]
#truncate side scatter at 500
fcs <- fcs[fcs@exprs[,'SSC-A']>500,]
#truncate forward scatter at 100
fcs <- fcs[fcs@exprs[,'FSC-A']>100,]
#truncate side scatter at 15000
#fcs <- fcs[fcs@exprs[,'SSC-A']>15000,]
#fcs <- fcs[fcs@exprs[,'SSC-A']<100000,]
#fcs <- fcs[fcs@exprs[,'FSC-A']>40000,]
return(fcs)
}
fcs.filter <- function(fcs) {
#remove min & max scatter
min.ssc <- min(fcs@exprs[,'SSC-A'])
max.ssc <- max(fcs@exprs[,'SSC-A'])
fcs <- fcs[min.ssc < fcs@exprs[,'SSC-A'] & fcs@exprs[,'SSC-A'] < max.ssc,]
min.fsc <- min(fcs@exprs[,'FSC-A'])
max.fsc <- max(fcs@exprs[,'FSC-A'])
fcs <- fcs[min.fsc < fcs@exprs[,'FSC-A'] & fcs@exprs[,'FSC-A'] < max.fsc,]
return(fcs)
}
# Wrapper for the flowCore read.FCS function.
# Optionally applies compensation, transforms
# (this needs to be a parameter)
#
# Returns
read.FCS <- function(file, EXPRS=TRUE, FILTER=FALSE, channels=c(), COMP=TRUE, verbose=FALSE, TRANS=logicleTransform(w=0.1), spillover=NULL, ...) {
if (verbose) {
fcs <- flowCore::read.FCS(file, ...)
print(fcs@description$FCSversion)
} else {
fcs <- suppressWarnings(flowCore::read.FCS(file, ...))
}
params <- fcs@parameters
pd <- pData(params)
# Replace any null descs with names (for FSC-A, FSC-W, SSC-A)
bad_col <- grep("^[a-zA-Z0-9]+",pd$desc,invert=TRUE)
if (length(bad_col) > 0) {
keyval <- keyword(fcs)
for (i in bad_col) {
pd$desc[i] <- pd$name[i]
keyval[[paste("$P",i,"S",sep="")]] <- pd$name[i]
}
pData(params) <- pd
fcs <- flowFrame(exprs(fcs),params,description=description(fcs))
keyword(fcs) <- keyval
}
#if FMO is present Marcin tells me this means he forgot a stain
if ( 'FMO' %in% colnames(getChannels(fcs)) ) {
warning('FMO channel! Returning NULL.\n')
return(NULL)
}
# Compensate data if SPILL or SPILLOVER present, stripping compensation matrix
# out of the flowFrame, i.e we should only have to do this once
apply.comp <- function(in_fcs, keyword) {
if (is.null(spillover)) {
comp_fcs <- compensate(in_fcs, description(in_fcs)[[keyword]])
} else {
print(description(in_fcs)[[keyword]])
comp_fcs <- compensate(in_fcs, spillover)
}
#flowFrame(exprs(comp_fcs), parameters(comp_fcs), description(comp_fcs)[grep("SPILL",names(description(comp_fcs)),invert=TRUE)])
flowFrame(exprs(comp_fcs), comp_fcs@parameters, description(comp_fcs)[grep("SPILL",names(description(comp_fcs)),invert=TRUE)])
}
if (COMP && !is.null(description(fcs)$SPILL)) {
fcs <- apply.comp(fcs, "SPILL")
} else if (COMP && !is.null(description(fcs)$SPILLOVER)) {
fcs <- apply.comp(fcs, "SPILLOVER")
}
#some filtering happens here
dim(fcs)
if (fcs@description$FCSversion=='3') {
if (FILTER) fcs <- fcs3.filter(fcs)
for (channel in channels) {
#i <- grep( channel, parameters(fcs)@data$desc, ignore.case=T )
if (is.null(TRANS)) trans <- identity
else if (channel == 'FSCA') trans <- function(x) 5*x/262144
else if (channel == 'SSCA') trans <- log10
else trans <- TRANS
#fcs@exprs[,i] <- trans(fcs@exprs[,i])
fcs <- setChannels(fcs, channel, trans(getChannels(fcs, channel)))
}
} else if (fcs@description$FCSversion=='2') {
for (channel in channels) {
#if (channel == 'FSC-A') trans <- function(x) 5*x/262144
if (is.null(TRANS)) trans <- identity
else if (channel == 'FSCA') trans <- identity
else if (channel == 'SSCA') trans <-identity
else trans <- log10
#fcs@exprs[,i] <- trans(fcs@exprs[,i])
fcs <- setChannels(fcs, channel, trans(getChannels(fcs, channel)))
}
}
if (FILTER) fcs <- fcs.filter(fcs)
dim(fcs)
if (EXPRS) return(getChannels(fcs, channels=channels))
else return(fcs)
}
#returns a matrix
read.flow.file <- function(filename,channels) {
cat('File extension', file.ext <- tolower(file_ext(filename)), '\n')
if (file.ext=='fcs') {
#fcs file
print(head(fcs.data <- getChannels(read.FCS(filename, channels=channels),channels)))
#My version of read.FCS discards events on axis if filtered, so total count is less that what is in FCS file initially.
print(total.count <- as.numeric(read.FCSheader(filename,keyword='$TOT')))
} else if (file.ext=='csv') {
#csv file
print(head(fcs.data <- read.csv(filename)[,channels]))
print(total.count <- dim(fcs.data)[[1]])
} else if (file.ext=='rdata') {
print(load(filename))
if (exists('result'))
print(head( fcs.data <- result$d.original[result$lymph.filter,][result$singlet.filter,channels] ))
else
print(head( fcs.data <- fcs.data[,channels] ))
print(total.count <- dim(fcs.data)[[1]])
} else {
stop('Unsupported file extension', file.ext,'\n')
}
return(fcs.data)
}
# Wrapper for the flowCore read.FCS function.
# Assumes that FSC-A and SSC-A always exits in file.
# Applies compensation
# Returns matrix
read.FCS.matrix <- function(file, channels=NULL, comp=TRUE, verbose=FALSE, ...) {
if (!file.exists(file)) stop(paste(file, 'does not exist!\n'))
if (verbose) print(dim(fcs <- flowCore::read.FCS(file, ...)))
else fcs <- suppressWarnings(flowCore::read.FCS(file, ...))
# Compensate data if SPILL or SPILLOVER present, stripping compensation matrix
# out of the flowFrame, i.e we should only have to do this once
apply.comp <- function(in_fcs, keyword) {
comp_fcs <- compensate(in_fcs, description(in_fcs)[[keyword]])
#flowFrame(exprs(comp_fcs), parameters(comp_fcs), description(comp_fcs)[grep("SPILL",names(description(comp_fcs)),invert=TRUE)])
flowFrame(exprs(comp_fcs), comp_fcs@parameters, description(comp_fcs)[grep("SPILL",names(description(comp_fcs)),invert=TRUE)])
}
if (comp && !is.null(description(fcs)$SPILL)) {
fcs <- apply.comp(fcs, "SPILL")
} else if (comp && !is.null(description(fcs)$SPILLOVER)) {
fcs <- apply.comp(fcs, "SPILLOVER")
}
params <- fcs@parameters
pd <- pData(params)
# Replace any null descs with names (for FSC-A, FSC-W, SSC-A)
bad_col <- grep("^[a-zA-Z0-9]+",pd$desc,invert=TRUE)
if (length(bad_col) > 0) {
keyval <- keyword(fcs)
for (i in bad_col) {
pd$desc[i] <- pd$name[i]
keyval[[paste("$P",i,"S",sep="")]] <- pd$name[i]
}
pData(params) <- pd
fcs <- flowFrame(exprs(fcs),params,description=description(fcs))
keyword(fcs) <- keyval
}
#if FMO is present Marcin tells me this means he forgot a stain
if ( 'FMO' %in% colnames(getChannels(fcs)) ) {
warning('FMO channel! Returning NULL.\n')
return(NULL)
}
# only keep the data matrix
X <- fcs@exprs
colnames(X) <- gsub('[- ]', '.', toupper(fcs@parameters@data$desc))
#remove min & max scatter
min.ssc <- min(X[,'SSC.A'])
max.ssc <- max(X[,'SSC.A'])
X <- X[min.ssc < X[,'SSC.A'] & X[,'SSC.A'] < max.ssc,]
min.fsc <- min(X[,'FSC.A'])
max.fsc <- max(X[,'FSC.A'])
X <- X[min.fsc < X[,'FSC.A'] & X[,'FSC.A'] < max.fsc,]
# do the transforms based on the fcs file format
if (fcs@description$FCSversion=='3') {
#remove negative scatter
X <- X[X[,'SSC.A']>1,]
X <- X[X[,'FSC.A']>1,]
#truncate side scatter at 500
X <- X[X[,'SSC.A']>500,]
#truncate forward scatter at 100
fcs <- fcs[fcs@exprs[,'FSC.A']>100,]
#truncate side scatter at 15000
for (channel in colnames(X)) {
if (channel == 'FSC.A') trans <- function(x) 5*x/262144
else if (channel == 'SSC.A') trans <- log10
else if (channel == 'TIME') trans <- identity
else trans <- lgcl
X[,channel] <- trans(X[,channel])
}
} else if (fcs@description$FCSversion=='2') {
for (channel in colnames(X)) {
if (channel == 'FSC.A') trans <- identity
else if (channel == 'SSC.A') trans <-identity
else if (channel == 'TIME') trans <- identity
else trans <- log10
X[,channel] <- trans(X[,channel])
}
}
for (channel in colnames(X)) {
x <- X[,channel]
X <- X[min(x) < x & x < max(x),]
}
if (is.null(channels)) return(X)
else return(X[,channels])
}
build.flowFrame <- function(x) {
if (!is.matrix(x)) {
stop("Input must be matrix")
}
# Build metadata for FCS file
pd <- c() # 'params' phenoData
dl <- list() # 'description' list
dl[["$DATATYPE"]] <- "F"
for (c in 1:ncol(x)) {
c_name <- colnames(x)[c]
c_min <- min(x[,c])
c_max <- max(x[,c])
c_rng <- c_max - c_min + 1
pl <- matrix(c(c_name, c_name, c_rng, c_min, c_max),nrow=1)
colnames(pl) <- c("name", "desc", "range", "minRange", "maxRange")
rownames(pl) <- paste("$P",c,sep="")
pd <- rbind(pd, pl)
dl[[paste("$P",c,"B",sep="")]] <- "32"; # Number of bits
dl[[paste("$P",c,"R",sep="")]] <- toString(c_rng); # Range
dl[[paste("$P",c,"E",sep="")]] <- "0,0"; # Exponent
dl[[paste("$P",c,"N",sep="")]] <- c_name; # Name
dl[[paste("$P",c,"S",sep="")]] <- c_name; # Desc
}
flowFrame(x, as(data.frame(pd), "AnnotatedDataFrame"), description=dl)
}
getIndividual <- function(x) x@description$INDIVIDUAL
getDose <- function(x) x@description$DOSE
getDate <- function(x) {
if (class(x)=='flowCore')
format(as.Date(dmy(x@description$`$DATE`, quiet=T)), '%Y-%m-%d')
else if (class(x)=='character')
format(as.Date(dmy(flowCore::read.FCS(x)@description$`$DATE`, quiet=T)), '%Y-%m-%d')
}
getSSC <- function(x) x@exprs[,'SSC-A']
getFSC <- function(x) x@exprs[,'FSC-A']
matchChannelNames <- function(channels, fcs.channels) {
matches <- c()
channels <- tolower(channels)
fcs.channels <- tolower(fcs.channels)
for (chan in channels) {
if (length(m <- which(chan == fcs.channels))) {
matches <- c(matches, m)
} else if (length(m <- grep(chan, fcs.channels,value=TRUE))) {
m <- m[which.min(sapply(m, length))]
matches <- c(matches, which(m==fcs.channels))
} else {
warning(paste('could not find', chan, 'in', paste(fcs.channels, collapse=',')))
}
}
return(matches)
}
getChannels <- function(fcs.data, channels) {
if (is(fcs.data,'matrix') || is(fcs.data,'data.frame')) {
if (missing(channels)) channels <- colnames(fcs.data)
channels <- gsub('-', '',paste(channels, ifelse(!grepl('-', channels), '$', ''), sep=''))
fcs.channels <- gsub('-','',colnames(fcs.data))
x <- as.matrix(fcs.data[,sapply(channels, function(x) grep(x, fcs.channels, ignore.case=T))])
} else if (is(fcs.data, 'flowFrame')) {
fcs.channels <- gsub('-','',fcs.data@parameters@data$desc)
if (missing(channels)) {
channels <- fcs.channels
x <- as.matrix( fcs.data@exprs[,sapply(channels, function(x) match(x, fcs.channels))] )
} else {
matches <- matchChannelNames(channels, fcs.channels)
x <- as.matrix(fcs.data@exprs[,matches])
}
} else {
stop('Unrecognised type:', class(fcs.data))
}
colnames(x) <- gsub('\\$','',channels)
x
}
exprs <- function(fcs.data) {
if (class(fcs.data)=='matrix')
return(fcs.data)
else
return(fcs.data@exprs)
}
setChannels <- function(fcs.data, channels, x) {
#colnames(x) <- gsub('\\$','',channels)
#channels <- gsub('-','',paste(channels, ifelse(!grepl('-', channels), '$', ''), sep=''))
channels <- gsub('-','',channels)
if (is(fcs.data,'matrix') || is(fcs.data,'data.frame')) {
fcs.channels <- gsub('-','',colnames(fcs.data))
fcs.data[,sapply(channels, function(x) grep( x, fcs.channels, ignore.case=T ))] <- x
} else if (is(fcs.data, 'flowFrame')) {
fcs.channels <- gsub('-','',fcs.data@parameters@data$desc)
#fcs.data@exprs[,sapply(channels, function(x) grep( x, fcs.channels, ignore.case=T ))] <- x
fcs.data@exprs[,matchChannelNames(channels,fcs.channels)] <- x
}
fcs.data
}
getChannelsf <- function(fcs.data, channels, f) {
return( apply(getChannels(fcs.data, channels), 2, f) )
}
getMFI <- function(fcs.data, channels) mean(getChannels(fcs.data, channels))
update.FCS.description <- function(file.name, keywords, read=FALSE) {
fcs.file <- flowCore::read.FCS(file.name)
for (k in names(keywords)) {
fcs.file@description[[k]] <- keywords[[k]]
}
write.FCS(fcs.file, filename=file.name)
if (read) {
read.FCS(fcs.file)
}
}
downsample.indexes <- function(fcs.data, density, desired_samples=20000, verbose=TRUE) {
if (is(fcs.data,'flowFrame')) fcs.data <- fcs.data@exprs
if (missing(density)) density <- fcs.data$dens
fcs.data <- cbind(fcs.data, density)
# exclude_pctile
boundary1 <- quantile(density,0.01,names=FALSE)
out_data <- subset(fcs.data, density > boundary1) # Exclusion
if (desired_samples >= nrow(out_data)) return( which(density > boundary1) )
# Need to find target density such there are approximately desired_samples
# remaining after downsampling. To do so we solve for the density such that
# the sum of samples below that density plus the expected value of
# samples retained above that density equals approximately the desired
# number of samples
density_s <- sort( out_data[,'density'] )
cdf <- rev(cumsum(1.0/rev(density_s)))
# Default solution if target density smaller than any present
boundary2 <- desired_samples/cdf[1]
# Boundary actually falls amongst densities present
if (boundary2 > density_s[1]) {
targets <- (desired_samples-1:length(density_s)) / cdf
boundary2 <- targets[which.min(targets-density_s > 0)]
}
if (verbose) print(c(boundary1, boundary2))
return( which( (boundary1 < density) & (boundary2/density > runif(length(density))) ) )
}
# Main scatterplot
marginal.2D.density <- function(fcs.data, channels, dir) {
dir.create(dir, recursive=T, showWarnings=F)
comb <- t(combn(channels, 2))
for (i in 1:nrow(comb)) {
channels <- comb[i,]
print(channels)
d <- data.frame(getChannels(fcs.data, channels))
d$col <- ifelse('col' %in% colnames(fcs.data@exprs), as.factor(fcs.data@exprs[,'col']), 'black')
colnames(d) <- c('x', 'y')
p1 <- ggplot(d, aes(x, y)) +
xlab(channels[1]) +
ylab(channels[2]) +
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
expand_limits(y = c(min(d$x) - .1*diff(range(d$x)), max(d$x) + .1*diff(range(d$x)))) +
expand_limits(x = c(min(d$y) - .1*diff(range(d$y)), max(d$y) + .1*diff(range(d$y)))) +
theme(plot.margin= unit(c(.2, .2, .5, .5), "lines"), plot.background=element_rect(fill='white')) +
#geom_point(data=d, colour='black') +
geom_density2d(data=d, colour='black')
# Horizontal marginal density plot - to appear at the top of the chart
p2 <- ggplot(d, aes(x = x)) +
geom_density() +
scale_x_continuous(expand = c(0, 0)) +
expand_limits(x = c(min(d$y) - .1*diff(range(d$y)), max(d$y) + .1*diff(range(d$y)))) +
theme(axis.text = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
plot.margin= unit(c(1, .2, -.5, 0.5), "lines"),
plot.background=element_rect(fill='white'))
# Vertical marginal density plot - to appear at the right of the chart
p3 <- ggplot(d, aes(x = y)) +
geom_density() +
scale_x_continuous(expand = c(0, 0)) +
expand_limits(x = c(min(d$x) - .1*diff(range(d$x)), max(d$x) + .1*diff(range(d$x)))) +
coord_flip() +
theme(axis.text = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
plot.margin= unit(c(.2, 1, 0.5, -.5), "lines"),
plot.background=element_rect(fill='white'))
# Get the gtables
gt1 <- ggplot_gtable(ggplot_build(p1))
gt2 <- ggplot_gtable(ggplot_build(p2))
gt3 <- ggplot_gtable(ggplot_build(p3))
# Get maximum widths and heights for x-axis and y-axis title and text
maxWidth = unit.pmax(gt1$widths[2:3], gt2$widths[2:3])
maxHeight = unit.pmax(gt1$heights[4:5], gt3$heights[4:5])
# Set the maximums in the gtables for gt1, gt2 and gt3
gt1$widths[2:3] <- as.list(maxWidth)
gt2$widths[2:3] <- as.list(maxWidth)
gt1$heights[4:5] <- as.list(maxHeight)
gt3$heights[4:5] <- as.list(maxHeight)
# Combine the scatterplot with the two marginal boxplots
# Create a new gtable
gt <- gtable(widths = unit(c(7, 2), "null"), height = unit(c(2, 7), "null"))
# Instert gt1, gt2 and gt3 into the new gtable
gt <- gtable_add_grob(gt, gt1, 2, 1)
gt <- gtable_add_grob(gt, gt2, 1, 1)
gt <- gtable_add_grob(gt, gt3, 2, 2)
# And render the plot
pdf(file.path(dir, sprintf('%s.pdf',paste(channels, collapse='-'))))
print(grid.newpage())
print(grid.draw(gt))
print(grid.rect(x = 0.5, y = 0.5, height = .995, width = .995, default.units = "npc", gp = gpar(col="black", fill = NA, lwd = 1)) )
dev.off()
}
}
### plot clusters for all channels in panel
plot.clusters <- function(x, clustering, panel, pdf.filename, width=15, height=15) {
x <- data.frame(getChannels(x, getPanelChannels(panel)))
x$cluster <- as.character(clustering)
x <- na.omit(x)
pdf(pdf.filename, width=width, height=height)
print(ggpairs(x, columns=getPanelChannels(panel), colour='cluster', lower=list(continuous='density'), axisLabels='none', upper=list(continuous='blank')))
dev.off()
}
pairs.smoothScatter <- function(x,hdr=FALSE) print( pairs(x, lower.panel=NULL, upper.panel = function(x,y, ...) {
smoothScatter(x,y, ..., nrpoints = 0, add = TRUE)
if(hdr) {
X <- cbind(x,y)
fs <- featureSignif(X)
points(X[fs$curvData,],pch=20,col='red')
} }) )
#pairs.featureSignif <- function(x) print( pairs(x, panel = function(x,y,...) { par(new=TRUE); plot(featureSignif(cbind(x,y))); }) )
###
plotClusters1D <- function(x, classification=NULL, fun=normalised.density, ...) {
plot(fun(x), ...)
if (!is.null(classification))
for (k in sort(unique(classification))) {
X1 <- x[which(classification==k)]
if (length(X1)>10) lines(fun(X1), col=k, lwd=2)
else points(cbind(X1,0), col=k, pch=20)
}
}
###
### Can draw the convex hull or the ellipse (based on the cluster covariance) for a cluster.
plotClusters <- function(x, plot.points=NULL, plot.points.col='black', plot.points.pch=20, plot.file=NULL, classification=NULL, posteriors=NULL, posterior.cutoff=.95, outliers=FALSE, ellipses=TRUE, clusters.col=NULL, chulls=TRUE, chull.lwd=.5, upper=smoothPlot, lower=NULL, PAR=TRUE, ...) {
if (class(x)=='numeric') {
par(mfrow=c(1,1))
plotClusters1D(x, classification, ...)
return(NULL)
}
if (is.null(colnames(x))) colnames(x) <- as.character(1:ncol(x))
col.names <- colnames(x)
nc <- ncol(x)
cat('>>',plot.file,'\n')
if (!is.null(plot.file)) png(plot.file)
if (PAR) par(mfrow = c(nc, nc), mai=c(.2,.2,.2,.2), oma=c(2,3,2,.5))
# plots the clusters either as chulls or as ellipses
f <- function(dim.inds) {
if(is.null(classification)) cols <- 'black' else cols <- classification
if (!is.null(plot.points)) points(plot.points[,col.names[dim.inds]], col=plot.points.col, pch=plot.points.pch)
if (!is.null(classification))
for (k in sort(unique(classification))) {
if (is.null(clusters.col)) col <- k
else col <- clusters.col[[k]]
X1 <- x[which(classification==k),dim.inds]
if (chulls) {
p <- X1[chull(X1),]
p <- rbind(p, p)
lines(p, col=col, lwd=chull.lwd)
}
#also approximate with an ellipse
if (ellipses) lines(ellipse::ellipse(cov(X1),centre=colMeans(X1)),col=col,lwd=2,lty=1)
}
if (!is.null(posteriors))
for (k in 1:ncol(posteriors)) {
if (is.null(clusters.col)) col <- k
else col <- clusters.col[[k]]
i <- which(posteriors[,k]>posterior.cutoff)
if (length(i)>2) {
X1 <- x[i,dim.inds]
if (chulls) {
#make sure chull is not open
ch <- c(chull(X1),chull(X1))
p <- X1[ch,]
lines(p, col=col, lwd=chull.lwd)
}
#also approximate with an ellipse
if (ellipses) lines(ellipse::ellipse(cov(X1),centre=colMeans(X1)),col=col,lwd=2,lty=1)
}
}
}
for (n in 1:length(col.names)) {
ylab <- ''
xlab <- ''
main <- ''
for (n2 in 1:length(col.names)) {
dim.inds <- c(n2,n)
#upper diagonal terms containing bivariate densities
if ( n < n2 ) {
if (is.null(upper)) {
frame()
} else {
upper(x[,dim.inds],main=main,xlab=xlab,ylab=ylab, outliers=outliers, classification=classification, ellipses=ellipses, clusters.col=clusters.col, chulls=chulls, ...)
f(dim.inds)
}
#lower diagonal terms containing bivariate densities
} else if ( n > n2 ) {
if (is.null(lower)) {
frame()
} else {
lower(x[,dim.inds],main=main,xlab=xlab,ylab=ylab, outliers=outliers, clusters.col=clusters.col, ...)
f(dim.inds)
}
#diagonal contains univariate densities
# } else if (n == n2) {
} else {
#diagonal(x[,n])
q1 <- quantile(x[,n],probs=c(0.01,0.99))
d <- normalised.density(x[,n])
if (!outliers)
plot(d,main=main,xlab=xlab,ylab=ylab,xlim=c(q1[['1%']],q1[['99%']]))
else
plot(d,main=main,xlab=xlab,ylab=ylab)
if (!is.null(classification))
for (k in sort(unique(classification))) {
X1 <- x[which(classification==k),n]
if (length(X1)>10) lines(normalised.density(X1), col=k, lwd=2)
else points(cbind(X1,0), col=k, pch=20)
}
if (!is.null(posteriors))
for (k in 1:ncol(posteriors)) {
X1 <- x[posteriors[,k]>posterior.cutoff,n]
if (is.null(clusters.col)) col <- k
else col <- clusters.col[k]
if (length(X1)>10) lines(normalised.density(X1), col=col, lwd=2)
else points(cbind(X1,0), col=col, pch=20)
}
}
if (n==1) mtext(col.names[[n2]], side=3, cex=.75, line=1)
if (n2==1) mtext(col.names[[n]], side=2, cex=.75, line=2)
}
}
if (!is.null(plot.file)) dev.off()
if (PAR) par(mfrow=c(1,1))
}
### Supports flowClust and Mclust res.
### Can apply model to new data.
### @parameter
plotClustRes <- function(x, res, before.res=NULL, outliers=TRUE, plot.file=NULL, cex=2) {
print(col.names <- colnames(x))
nc <- ncol(x)
if (class(res)=='Mclust') {
K <- 1:res$G
p <- predict(res, x)
print(table(clustering <- p$classification))
} else if (class(res)=='flowClust') {
K <- 1:res@K
x <- box(x, res@lambda)
#col.names <- colnames(x)
print(col.names <- res@varNames)
nc <- length(col.names)
x <- x[,col.names]
if (!outliers) x <- x[which(!res@flagOutliers),]
print(head(clustering <- MAP(x, res)))
if (!is.null(before.res)) print(head(before.clustering <- MAP(x, before.res)))
}
cat('>>',plot.file,'\n')
if (!is.null(plot.file)) match.fun(file_ext(plot.file))(plot.file)
par(mfrow = c(nc, nc), mai=c(.2,.2,.2,.2), oma=c(2,3,2,.5))
for (n in 1:length(col.names)) {
ylab <- ''
xlab <- ''
main <- ''
for (n2 in 1:length(col.names)) {
#off-diagonal terms containing bivariate densities
if (n < n2) {
dim.inds <- c(n2,n)
smoothScatter(x[,dim.inds],main=main,xlab=xlab,ylab=ylab,colramp=colorRampPalette(c('white','blue','green','yellow','orange','red')))
if (class(res)=='Mclust') {
for (i in K) {
points(t(as.matrix(res$parameters$mean[dim.inds,i])),pch=20,col=i)
lines(ellipse::ellipse(res$parameters$variance$sigma[dim.inds,dim.inds,i],centre=t(res$parameters$mean[dim.inds,i])),col=i,lwd=2,lty=1)
}
} else if (class(res)=='flowClust') {
if(!is.null(before.res))
for (i in K) {
points(t(as.matrix(before.res@mu[i,dim.inds])),pch=20,col=i)
mu <- before.res@mu[i,dim.inds]
sigma <- before.res@sigma[i,dim.inds,dim.inds]
lines(ellipse::ellipse(sigma,centre=mu),col=i,lwd=2,lty=2)
}
for (i in K) {
points(t(as.matrix(res@mu[i,dim.inds])),pch=20,col=i)
mu <- res@mu[i,dim.inds]
sigma <- res@sigma[i,dim.inds,dim.inds]
lines(ellipse::ellipse(sigma,centre=mu),col=i,lwd=2,lty=1)
}
}
} else if (n > n2) {
plot( NULL, xlim=range(x[,n2]), ylim=range(x[,n]), xaxt='n', yaxt='n', ann=FALSE, bty='n' )
#diagonal contains univariate densities
} else {
d <- normalised.density(x[,n])
plot(d,main=main,xlab=xlab,ylab=ylab)
if (class(res)=='Mclust') {
for (i in K) {
lines(normalised.density(x[which(clustering==i),n]), col=i, lwd=2)
}
} else if (class(res)=='flowClust') {
for (i in K) {
cl <- which(clustering==i)
if (length(cl)>2) lines(normalised.density(x[cl,n]), col=i, lwd=2)
}
if (!is.null(before.res))
for (i in K) {
cl <- which(before.clustering==i)
if (length(cl)>2) lines(normalised.density(x[cl,n]), col=i, lwd=2, lty=2)
}
}
}
if (n==1) mtext(col.names[[n2]], side=3, cex=cex, line=1)
if (n2==1) mtext(col.names[[n]], side=2, cex=cex, line=2)
}
}
if (!is.null(plot.file)) dev.off()
}
### density at each point for each flowClust component
compute.dens <- function(d, res) sapply(1:res@K, function(i) res@w[i]*flowClust::dmvt(d, mu=res@mu[i,], sigma=res@sigma[i,,], nu=res@nu, lambda=res@lambda)$value)
### outliers are points which follow below a given quantile of density
outliers <- function(d, res, dens.threshold=.05) {
dens <- compute.dens(d, res)
quants <- quantile(dens, probs=seq(0,1,dens.threshold))
return(dens<quants[[2]])
}
### outliers are points which lie beyond a certain distance from the
### posterior of each point for each flowClust component
compute.post <- function(d, res) {
dens <- compute.dens(d, res)
total.dens <- rowSums(dens)
dens/total.dens
}
### return the component assignment
MAP <- function(d, res, post.threshold=NULL) {
d <- d[,res@varNames]
d <- box(d, res@lambda)
print(head(post <- compute.post(d,res)))
if (is.null(post.threshold))
apply(post,1,which.max)
else
apply(post,1,function(x)ifelse(length(which(x>post.threshold))>0,which.max(x),NA))
}
dens <- function(x,bw) {
o<-apply(x,2,order)
x<-apply(x,2,sort)
d <- apply(x,2, function(x) {
count <- c()
i <- 1
while (i <= length(x)) {
if (1 < i && x[i]-x[i-1] < bw**10) {
count <- c(count,count[length(count)])
} else {
#forwards
j <- k <- i
while( j <= length(x) && x[j]-x[i] < bw ) j<-j+1
#backwards
while( 1 <= k && x[i]-x[k] < bw ) k<-k-1
count <- c(count,j-k)
}
(i<-i+1)
}
return(count)
})
d <- t(t(sapply(1:ncol(x), function(i) d[o[,i],i])))
colnames(d) <- colnames(x)
return(sweep(d, 2, colSums(d), '/'))
#return(d)
}
dens <- function(x) {
d <- apply(x,2, function(x) {
d <- density(x)
f <- splinefun(d$x,d$y)
f(x)
})
m <- apply(sweep(d, 2, colSums(d), '/'), 1, function(i) colnames(x)[which.max(i)])
#apply(sweep(d, 2, colSums(d), '/'), 1, function(i) mean(i)/var(i))
w <- prop.table(table(m))
w <- 1/w
w <- w/sum(w)
d <- t(apply(d, 1, function(x) x*w))
sweep(d, 2, colSums(d), '/')
}
dens <- function(x, bw=.1, n=512) {
d <- apply(x, 2, function(x) density(x,bw,n)$y)
return(sweep(d, 2, colSums(d), '/'))
}
filter.mahalanobis <- function(x, mu, sigma, p=.999) {
d <- mahalanobis(x,mu,sigma)
return(d<qchisq(p, df=length(mu)))
}
filter.mahalanobis.ellipse <- function(x, e, p=.999) {
d <- mahalanobis(x[,names(e$Mu)],e$Mu,e$Sigma)
return(d<qchisq(p, df=length(e$Mu)))
}
mahalanobis.magnetic.ellipse <- function(x, e, p=.999) {
#Mu is updated 3 times
d <- mahalanobis(x[,names(e$Mu)],e$Mu,e$Sigma)
e$Mu <- colMeans(x[which(d<qchisq(p, df=length(e$Mu))),names(e$Mu)])
d <- mahalanobis(x[,names(e$Mu)],e$Mu,e$Sigma)
e$Mu <- colMeans(x[which(d<qchisq(p, df=length(e$Mu))),names(e$Mu)])
d <- mahalanobis(x[,names(e$Mu)],e$Mu,e$Sigma)
e$Mu <- colMeans(x[which(d<qchisq(p, df=length(e$Mu))),names(e$Mu)])
return(e)
}
kmeans.magnetic.ellipses <- function(x, ellipses) {
#repeat 3 times
(centers <- do.call('rbind', lapply(ellipses, function(e) e$Mu)))
res <- kmeans(x[,colnames(centers)], centers=centers)
for (i in 1:nrow(centers)) ellipses[[i]]$Mu <- res$centers[i,]
return(ellipses)
}
em.magnetic.ellipses <- function(x, gates, iterations=10, background=NULL) {
tau <- sapply(gates, function(x) x$tau)
for (j in 1:length(gates)) gates[[j]]$tau <- tau[[j]]/sum(tau)
if (!is.null(background)) gates[[length(gates)+1]] <- list(Mu=colMeans(x),Sigma=cov(x),tau=background)
for (i in 1:iterations) {
#u <- 1/mahalanobis(
dens <- sapply(gates, function(e) e$tau*mixtools::dmvnorm(x[,names(e$Mu)], mu=e$Mu, sigma=e$Sigma))
post <- t(apply(dens, 1, function(x) x/sum(x)))
for (j in 1:length(gates)) gates[[j]]$Mu <- apply(x[,names(gates[[j]]$Mu)], 2, weighted.mean, w=post[,j])
tau <- colSums(post)/sum(post)
for (j in 1:length(gates)) gates[[j]]$tau <- tau[[j]]
}
return(gates)
}
filter.mahalanobis.ellipses <- function(x, ellipses, p=.999) {
rowSums(sapply( ellipses, function(e) mahalanobis(x[,names(e$Mu)],e$Mu,e$Sigma)<qchisq(p, df=length(e$Mu))))==length(ellipses)
}
#fcs.data needs to be 2D
classification.to.chull <- function(fcs.data, clr) {
clr <- which(as.logical(clr))
d <- fcs.data[clr,]
return(d[chull(d),])
}
classification.to.ellipse <- function(fcs.data, clr) {
clr <- which(as.logical(clr))
d <- fcs.data[clr,]
return(list(Mu=colMeans(d), Sigma=cov(d), tau=nrow(d)/nrow(fcs.data)))
}
points.in.ellipse <- function(X, w, npoints=255, alpha=.05) {
e <- classification.to.ellipse( X, w )
mu <- e$Mu
sigma <- e$Sigma
es <- eigen(sigma)
e1 <- es$vec %*% diag(sqrt(es$val))
r1 <- sqrt(qchisq(1 - alpha, 2))
theta <- seq(0, 2 * pi, len = npoints)
v1 <- cbind(r1 * cos(theta), r1 * sin(theta))
pts = t(mu - (e1 %*% t(v1)))
point.in.polygon(X[,1],X[,2],pts[,1],pts[,2])
}
classification.to.gate <- function(fcs.data, clr) {
e <- classification.to.ellipse(fcs.data, clr)
return(which(filter.mahalanobis.ellipse(fcs.data, e)))
}
plot.ellipses <- function(gates, col='black') {
for (e in gates) lines(ellipse::ellipse(e$Sigma,centre=e$Mu),col=col,lwd=2,lty=2)
points(do.call('rbind', lapply(gates,function(x)x$Mu)), pch='X', cex=2, col=col)
}
plot.gate.chull <- function(X, classification) {
for (k in sort(unique(classification))) {
X1 <- X[which(classification==k),]
p <- X1[chull(X1),]
p <- rbind(p, p)
lines(p, col=k)
}
}
#also approximate with an ellipse
plot.gate.ellipse <- function(X, classification) {
for (k in sort(unique(classification))) {
X1 <- X[which(classification==k),]
lines(ellipse::ellipse(cov(X1),centre=colMeans(X1)),col=k,lwd=2,lty=1)
}
}
### TODO
plotManualGates <- function(fcs.data, clr) {
ngates <- length(gatenames <- colnames(clr))
figure.labels <- iter(paste(letters,')',sep=''))
par(mfrow=c(round(sqrt(ngates)),round(sqrt(ngates))))
for (gn in gatenames) {
print(gn)
print(name <- unlist(strsplit(gn, '_'))[[1]])
print(channels <- unlist(strsplit(unlist(strsplit(gn, '_'))[[2]],'\\.')))
print(head(fcs.data[,channels]))
xquant <- quantile(fcs.data[,channels[[1]]],probs=seq(0,1,.01))
yquant <- quantile(fcs.data[,channels[[2]]],probs=seq(0,1,.01))
print(xlim <- c(xquant[['1%']],xquant[['99%']]))
print(ylim <- c(yquant[['1%']],yquant[['99%']]))
smoothScatter( fcs.data[,channels], xlab=channels[[1]], ylab=channels[[2]], main=name, colramp=colorRampPalette(c('white','blue','green','yellow','orange','red')), nrpoints=0, xlim=xlim, ylim=ylim)
X <- fcs.data[which(as.logical(clr[,gn])),channels]
p <- X[chull(X),]
p <- rbind(p, p)
#lines(p, col='red', lwd=2)
lines(ellipse::ellipse(cov(X),centre=colMeans(X)),col='red',lwd=2,lty=1)
title(nextElem(figure.labels), adj=0)
}
}
### normalised.density plot
smoothPlot1D <- function( x, nrpoints=0, colramp=colorRampPalette(c('white','blue','green','yellow','orange','red')), plot.points=NULL, plot.points.col='black', classification=NULL, posteriors=NULL, posterior.cutoff=.95, outliers=FALSE, main='', xlab='', ylab='', clusters.col=NULL, col='black', ... ) {
xquant <- quantile(x,probs=c(0.01,0.99))
print(xlim <- c(xquant[['1%']],xquant[['99%']]))
q1 <- quantile(x,probs=c(0.01,0.99))
d <- normalised.density(x)
if (!outliers)
plot(d,main=main,xlab=xlab,ylab=ylab,xlim=c(q1[['1%']],q1[['99%']]),col=col,...)
else
plot(d,main=main,xlab=xlab,ylab=ylab,col=col,...)
if (!is.null(classification))
for (k in sort(unique(classification))) {