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ConsTaxonomyPlots.R
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ConsTaxonomyPlots.R
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########################################################################################
## This file is a part of YAP package of scripts. https://github.com/shpakoo/YAP
## Distributed under the MIT license: http://www.opensource.org/licenses/mit-license.php
## Copyright (c) 2011-2013 Sebastian Szpakowski
########################################################################################
library(fdrtool)
library(RColorBrewer)
library(ade4)
library(vegan)
library(randomForest)
library(gplots)
collookup=function(x, annotation, column="SampleID")
{
val = annotation[annotation$SampleID==x, column]
library(RColorBrewer)
allvals = sort(unique(annotation[, column]))
#mypalette <- colorRampPalette(brewer.pal(12, "Paired"), space="rgb")
mypalette = rainbow(length(allvals))
return (mypalette[allvals == val])
}
getRandomColors=function(num)
{
if (num==1)
{
if (exists ( ".colorcounter" , env = .GlobalEnv))
{
.colorcounter = get(".colorcounter", envir = .GlobalEnv)
}
else
{
.colorcounter = 0
}
library(RColorBrewer)
#mypalette<-((brewer.pal(9, "Set1")))
#.colorcounter = (.colorcounter %% 9) +1
mypalette <- colorRampPalette(brewer.pal(9, "Set1"), space="rgb")
mypalette = mypalette(20)
.colorcounter = (.colorcounter %% 20) +1
K = mypalette[.colorcounter]
assign(".colorcounter", .colorcounter, envir = .GlobalEnv)
return (K)
}
else if (num>12)
{
return (sample(rainbow(num)))
}
else
{
library(RColorBrewer)
mypalette<-(brewer.pal(12, "Paired"))
return (sample(mypalette, num))
}
return (sample(rainbow(num)))
}
getColors=function(x)
{
if (exists ( "taxoncolors" , env = .GlobalEnv))
{
taxoncolors = get("taxoncolors", envir = .GlobalEnv)
}
else
{
taxoncolors = list()
}
otpt = list()
for (taxon in x)
{
if (regexpr("\\.\\.\\.", taxon)>-1)
{
taxoncolors[taxon] = "black"
}
else if (taxon %in% names(taxoncolors))
{
#print (taxoncolors[taxon])
}
else
{
taxoncolors[taxon] = getRandomColors(1)
}
otpt = append(otpt, taxoncolors[taxon])
#print (otpt)
}
assign("taxoncolors", taxoncolors, envir = .GlobalEnv)
return (unlist(otpt))
}
dist.binary = function(x, ... )
{
return (dist(x, method="binary", ...))
}
fundist= function(x, ... )
{
return (vegdist(x, method="bray", ...))
}
funhclust = function(x, ... )
{
return (hclust(x, method="average", ...))
}
makeLevelDataset=function(dataset, level=4, cumulative = FALSE)
{
if (! cumulative)
{
orig = dataset
toreturn = dataset[dataset$depth==level,]
OTHER = data.frame(rbind (rep(0, ncol(toreturn))))
names(OTHER) <- names(dataset)
toreturn=as.data.frame(rbind(toreturn, OTHER))
toreturn[nrow(toreturn), "label"] = "Unclassified"
for (taxonid in unique(dataset[dataset$depth==level,"taxonid"]))
{
tmp = orig[orig$depth>level, ]
tmp = tmp[regexpr(paste(taxonid, "\\.", sep=""), tmp$taxonid)==1,]
for (samp in names(toreturn)[4:ncol(toreturn)])
{
x = sum(tmp[,samp])
toreturn [toreturn$taxonid==taxonid, samp] = as.numeric(toreturn [toreturn$taxonid==taxonid, samp]) + x
}
}
for (samp in names(toreturn)[4:ncol(toreturn)])
{
x = sum(as.numeric(dataset [, samp])) - sum(as.numeric(toreturn [, samp]))
toreturn[toreturn$label=="Unclassified", samp] = x
}
return (toreturn)
}
else
{
tmp = dataset[dataset$depth==level,]
return (tmp)
}
}
ordering = function(...)
{
return (reorder(..., agglo.FUN = "max"))
}
makeHeatMap=function(dataset, annotation, highlight="SampleID", main="Heatmap", perc=TRUE)
{
tmp = prepareDataset(dataset)
mypalette <- colorRampPalette(c("blue","yellow", "red"), space="rgb")
mypalette = mypalette(20)
#### normalize
if (perc)
{
for (k in 1:ncol(tmp))
{
### the dataset is per taxon level!
### i.e. NOT cummulative
tmp[,k] = tmp[,k]/sum(tmp[,k])
}
lab = paste(round(seq(0,max(tmp*100), length.out=11),0), "%")
at = seq(0,max(tmp), length.out=11)
sumsfilter = (apply(tmp, 1, sd)>0.01)
}
else
{
sumsfilter = (apply(tmp, 1, sd)>0)
}
if (sum(sumsfilter)<3)
{
plot(0,0, axes=FALSE, type="n", xlab="", ylab="", main=paste(main, "nothing to plot", sep="\n") )
}
else
{
tmp = tmp[sumsfilter,]
tmp = data.matrix(tmp)
tmp = t(tmp)
tmp = tmp[row.names(tmp) %in% annotation$SampleID,]
colors = unlist( lapply(dimnames(tmp)[[1]], collookup, annotation, column=highlight) )
legendstuff = data.frame( fill = colors, SampleID = dimnames(tmp)[[1]], stringsAsFactors=F )
minianno = annotation[,c("SampleID", highlight)]
legendstuff = merge(legendstuff, minianno, by="SampleID", all.x=T, all.y=F)
legendstuff = aggregate(legendstuff, list(legendstuff[,highlight]), unique)
description = paste(main, ifelse(perc, "OTUs normalized per sample", "Raw counts"), "Bray-Curtis, Average Linkage", sep="\n")
#heatmap(tmp, col = mypalette, margins=c(20,20), keep.dendro=T, distfun = fundist, hclustfun = funhclust, scale=NULL, RowSideColors=colors, xlab="taxons", ylab="samples", main=paste(main, sep="\n"))
#legend("left", legend = legendstuff[,highlight], fill = legendstuff$fill, col = legendstuff$fill, cex=0.5)
#heatmap.2( xlab="taxons", ylab="samples", main=paste(main, sep="\n"))
heatmap.2(tmp, margins=c(25,5), distfun = fundist, hclustfun = funhclust, col=mypalette, RowSideColors=colors, trace="none", scale="none", tracecol="white", main = description)
legend("bottomleft", legend = legendstuff[,highlight], fill = legendstuff$fill, col = legendstuff$fill, cex=0.8, horiz=T )
}
}
mykde2d = function (dfxy, xax = 1, yax = 2, pch = 20, cpoint = 1, neig = NULL,
cneig = 2, xlim = NULL, ylim = NULL, grid = TRUE, addaxes = TRUE,
cgrid = 1, include.origin = TRUE, origin = c(0, 0), sub = "",
csub = 1.25, possub = "bottomleft", pixmap = NULL, contour = NULL,
area = NULL, add.plot = FALSE, ... )
{
if (!require(MASS))
stop("library MASS required for kde2d")
par(mar = c(0.1, 0.1, 0.1, 0.1))
s.label(dfxy, xax = xax, yax = yax, clab = 0, pch = pch,
cpoint = cpoint, neig = neig, cneig = cneig, xlim = xlim,
ylim = ylim, grid = grid, addaxes = addaxes, cgrid = cgrid,
include.origin = include.origin, origin = origin, sub = sub,
csub = csub, possub = possub, pixmap = pixmap, contour = contour,
area = area, add.plot = add.plot)
x <- as.numeric(dfxy[, xax])
y <- as.numeric(dfxy[, yax])
xykde = MASS::kde2d(x, y, lims = par("usr"))
zlim = range(xykde$z, finite = TRUE)
lev = seq(zlim[1], zlim[2], le = 8)
lev = lev[2:7]
contour(xykde, add = TRUE, levels = lev,
drawlabels = FALSE, ...)
invisible(match.call())
}
PCAPlot2=function(dataset, annotation, subs = "Sex", main="")
{
library(ade4)
tmp = prepareDataset(dataset)
sumsfilter = (apply(tmp, 1, sum)>0)
tmp=tmp[sumsfilter,]
tmp = data.matrix(tmp)
tmp = t(tmp)
tmp = tmp[row.names(tmp) %in% annotation$SampleID,]
ids = c()
for (k in row.names(tmp))
{
ids = append(ids, annotation[annotation$SampleID==k ,subs])
}
ids = as.factor(ids)
###anomalous, need GlobalEnv)
assign("ids", ids, envir=.GlobalEnv)
assign("tmp", tmp, envir=.GlobalEnv)
pca1 <- dudi.pca(tmp, center = TRUE, scale = FALSE, scan = FALSE)
pca2 <- dudi.pca(tmp, center = TRUE, scale = TRUE, scan = FALSE)
colors = unique(unlist( lapply(dimnames(tmp)[[1]], collookup, annotation, column=subs) ))
par(mfrow = c(2,2))
mykde2d(pca1$li, col="gray90", lwd=0.8, lty=1)
s.class(pca1$li, ids, cpoint = 1, sub = paste(main, "original", sep="\n"), csub=1, col=colors, add.p=TRUE, label="")
if (subs=="SampleID")
{
### no legend, dots will have text.
}
else if (length(levels(ids))>10)
{
legend("bottomright", levels(ids), fill=colors, ncol=2, cex=0.5 )
}
else
{
legend("bottomright", levels(ids), fill=colors, ncol=1, cex=0.7 )
}
text(pca1$li, rownames(pca1$li), cex=0.5, col="gray30", pos=3)
#s.corcircle(pca1$co, lab = dimnames(tmp)[[2]], full = TRUE, box = TRUE, sub = paste(main, "Contribution of variables to axes.",sep="\n"), csub=1)
s.arrow( pca1$c1, lab = dimnames(tmp)[[2]], sub = paste(main, "Importance of variable.", sep="\n") , csub=1, possub = "bottom")
mykde2d(pca2$li, col="gray90", lwd=0.8, lty=1)
s.class(pca2$li, ids, cpoint = 1, sub = paste(main,"scaled", sep="\n"), csub=1, col=colors, add.p=TRUE, label="")
if (subs=="SampleID")
{
### no legend, dots will have text.
}
else if (length(levels(ids))>10)
{
legend("bottomright", levels(ids), fill=colors, ncol=2, cex=0.5 )
}
else
{
legend("bottomright", levels(ids), fill=colors, ncol=1, cex=0.7 )
}
text(pca2$li, rownames(pca2$li), cex=0.5, col="gray30", pos=3)
s.corcircle(pca2$co, lab = dimnames(tmp)[[2]], full = TRUE, box = TRUE, sub = paste(main, "Contribution of variables to axes.",sep="\n"), csub=1, possub="bottom")
#s.arrow( pca2$c1, lab = dimnames(tmp)[[2]], sub = paste(main, "Importance of variable.", sep="\n") , csub=1)
par(mfrow = c(1,1))
return (pca1)
}
fixlabels=function(dataset)
{
for (r in 1:nrow(dataset))
{
tmp = dataset[r,]
label = tmp$label
id = unlist(strsplit(tmp$taxonid, "\\."))
parentlabel = ""
while (label == "unclassified")
{
parentid = paste(id[1:(length(id)-1)], collapse=".", sep=".")
#cat(label, id, "[",parentid, "] -> ")
parentlabel = dataset$label[dataset$taxonid==parentid]
#cat(parentlabel)
id = unlist(strsplit(parentid, "\\."))
label = parentlabel
#cat ("\n")
}
# print (tmp$label)
# print (tmp$depth)
# print (parentlabel)
#
if (parentlabel=="")
{
label = paste(tmp$label, " [", tmp$depth, "]", sep = "")
}
else
{
label = paste("Unclassified", " ", parentlabel, " [", tmp$depth, "]", sep = "")
}
dataset[r, "newlabel"] = label
}
dataset$label = dataset$newlabel
return (dataset[,-ncol(dataset)])
}
prepareDataset = function(dataset)
{
tmp = dataset[,4:ncol(dataset)]
x = aggregate (tmp, list(dataset$label), sum)
labs = x[,1]
x = x[,-1]
row.names(x) <- labs
return (x)
}
ordering =function(x)
{
return (sum(abs(diff(x))))
}
getPvals = function(x, mapping)
{
tmp = x
allheaders = unlist(mapping)
map = names(tmp)
for (k in 1:length(mapping))
{
if (length(mapping[[k]])==1)
{
for (id in mapping[[k]])
{
map = map[-which(map==id)]
tmp = tmp[-which(names(tmp)==id)]
}
}
else
{
for (id in mapping[[k]])
{
map[map==id]= k-1
}
}
}
pval = NA
# print ("...")
# print (x)
# print (mapping)
# print (tmp)
# print (as.numeric(map))
# print (sum(as.numeric(map)))
# print (sd(as.numeric(map)))
if (length(tmp)>0 && !is.na(sum(as.numeric(map))) && sd(as.numeric(map))>0 )
{
pval = kruskal.test(tmp, as.numeric(map) )$p.value
if (is.nan(pval))
{
return (NA)
}
}
return (pval)
}
profilePlot = function(dataset, annotation, desc, column, topmost=30, perc=TRUE, orderclasses=c(), legendtitle = paste("OTUs per ", "taxon", sep=""), statsfilename = "")
{
if (length(unique(annotation[,column]))==1)
{
return(data.frame())
}
tmp = prepareDataset(dataset)
if (length(orderclasses)>0)
{
}
else
{
orderclasses = unique(annotation[,column])
}
#print ("")
#print (tmp)
#print (orderclasses)
######## normalize
if (perc)
{
for (k in 1:ncol(tmp))
{
#print (".")
### the dataset is per taxon level!
### i.e. NOT cummulative
if (sum(tmp[,k])==0)
{
}
else
{
tmp[,k] = tmp[,k]/sum(tmp[,k])
}
}
lab = paste(round(seq(0,max(tmp*100), length.out=11),0), "%")
at = seq(0,max(tmp), length.out=11)
}
######### subset SampleIDs per group
x = aggregate(annotation[, c("SampleID")], list(annotation[,column]), function(x){ return(list(levels(x)[x])) } )
tmpsd=tmp
################
#print ("...")
#print (x)
# if (desc =="Taxon: level 3, samples grouped by: T1D")
# {
# save(tmp,x, annotation, column, file = "tmp.Rdata")
# }
pvals = apply(tmp[,names(tmp)%in% unlist(x[,2])], 1, getPvals, x[,2])
###############
######## aggregate statistics
Ns = list()
for (G in orderclasses)
{
# print (G)
group = x[x[,1]==G,]
members=unlist(group[,2])
Ns$new = length(members)
if (length(members)>1)
{
tmp$new = apply( tmp[, members], 1, mean)
tmpsd$new = apply(tmpsd[, members], 1, sd)
tmpsd$new = 1.96 * tmpsd$new /sqrt(length(members))
}
else
{
tmp$new=tmp[,members]
tmpsd$new = 0
}
names(tmp)[names(tmp)=="new"]<-c(group[,1])
names(tmpsd)[names(tmpsd)=="new"]<-c(group[,1])
names(Ns)[names(Ns)=="new"]<-c(group[,1])
}
######## keep only the aggregated statistics
tmp=tmp[,(ncol(tmp)-nrow(x)+1):ncol(tmp)]
tmpsd=tmpsd[,(ncol(tmpsd)-nrow(x)+1):ncol(tmpsd)]
###### table
otpt = data.frame(taxon = row.names(tmp) )
#otpt$taxon = row.names(tmp)
for (G in orderclasses)
{
#print (names(tmp))
#print (names(tmpsd))
#print (names(Ns))
#print (dim(otpt))
otpt.tmp = data.frame(tmp[,G], tmpsd[,G], Ns[[G]])
names(otpt.tmp) = c( paste(G, c("mean", "SD", "n"), sep="_" ) )
if (nrow(otpt)==0)
{
otpt = otpt.tmp
}
else
{
otpt = cbind(otpt, otpt.tmp)
}
}
otpt$pval = pvals
pvals = otpt$pval[!is.na(otpt$pval)]
if (length(pvals)>100)
{
U = fdrtool(pvals, statistic="pvalue", plot=FALSE, color.figure=FALSE, verbose=FALSE)
otpt$pval2[!is.na(otpt$pval)] = U$pval
otpt$qval[!is.na(otpt$pval)] = U$qval
otpt$lfdr[!is.na(otpt$pval)] = U$lfdr
}
else
{
otpt$pval2= NA
otpt$qval = NA
otpt$lfdr = NA
}
if (statsfilename != "")
{
write.table(otpt, paste(statsfilename, column, "stats.tab.txt", sep="." ), row.names=FALSE, col.names=TRUE, sep="\t", quote=FALSE )
}
######## order experiments (based on most differences between classes)
fororder = apply(tmp, 1, ordering)
tmp = tmp[order(fororder, decreasing=TRUE),]
tmpsd = tmpsd[order(fororder, decreasing=TRUE),]
#### plot only topX and accumulate the rest to form "Other"
if (topmost < nrow(tmp))
{
tmpA = tmp[1:(topmost+1) ,]
tmpB = tmp[(topmost+1):nrow(tmp),]
tmpA[topmost+1,] = apply(tmpB, 2, sum)
row.names(tmpA)[topmost+1] = paste("... [remaining" , nrow(tmpB),"]", sep=" ")
tmpsdA = tmpsd[1:(topmost+1) ,]
tmpsdA[topmost+1,] = rep(0, ncol(tmpsdA))
row.names(tmpsdA)[topmost+1] = paste("... [remaining" , nrow(tmpB),"]", sep=" ")
}
else
{
tmpA=tmp
tmpsdA = tmpsd
}
### reverse order so that the MOST changed are at the top
tmpA = tmpA[rev(1:nrow(tmpA)),]
tmpsdA = tmpsdA[rev(1:nrow(tmpsdA)),]
#print (dim(tmpA))
#print (dim(tmpsdA))
### flip so that data is grouped by taxa
tmpA = t(data.matrix(tmpA))
tmpsdA = t(data.matrix(tmpsdA))
### reverse the order of classes so that it matches the ordercolumn from top to bottom
tmpA = tmpA[rev(1:nrow(tmpA)),]
tmpsdA = tmpsdA[rev(1:nrow(tmpsdA)),]
#cols = getColors(row.names(tmpA))
barx = barplot(tmpA,
main=paste("Microbial profile on the taxonomic level of ",desc, sep=""),
legend = row.names(tmpA),
axes=FALSE,
cex.axis=0.8,
beside=TRUE,
horiz=TRUE,
las=1,
xlim = c(0,2.5*max(at)),
args.legend=list(cex=0.8, bg="ivory", title=legendtitle))
error.bar(barx, tmpA,tmpsdA, horiz=TRUE, col="black", lwd=3)
axis(1, at=at, lab=lab, las=3)
ticks = (1:ncol(tmpA))*(nrow(tmpA)+1) - nrow(tmpA)/2
axis(2, pos=0, at=ticks, lab=rep("",length(ticks)), lwd=5, col="gray80")
####repeat)
par(new=TRUE)
barx = barplot(tmpA,
main=paste("Microbial profile on the taxonomic level of ",desc, sep=""),
legend = row.names(tmpA),
axes=FALSE,
cex.axis=0.8,
beside=TRUE,
horiz=TRUE,
las=1,
xlim = c(0,2.5*max(at)),
args.legend=list(cex=0.8, bg="ivory", title=legendtitle))
invisible (list(means = tmpA, sds=tmpsdA))
#### pvalues and q values
forlab = dimnames(tmpA)[[2]]
forlab = data.frame(x = 1:length(forlab), taxon=forlab)
pvals = otpt[otpt$taxon %in% forlab$taxon, c("taxon", "pval", "qval")]
forlab = merge(forlab, pvals, by="taxon", all.x=FALSE, all.y=FALSE, sort=FALSE)
if (length(dimnames(tmpA)[[2]]) == nrow(forlab))
{
labs = c(round(forlab$pval,4))
}
else
{
labs = c(1, round(forlab$pval,4))
}
labs = unlist(lapply(labs, function(x) { ifelse( !is.na(x) && x<0.05, paste("p = ", format(x, nsmall=4, scientific=FALSE, zero.print=TRUE)), "" ) } ))
axis(2, pos=max(at)+0.15, at=ticks, lab=labs, lwd=0.5, col="gray90", las=2, cex.axis=1)
if (length(dimnames(tmpA)[[2]]) == nrow(forlab))
{
labs = c(round(forlab$qval,4))
}
else
{
labs = c(1, round(forlab$qval,4))
}
labs = unlist(lapply(labs, function(x) { ifelse( !is.na(x) && x<0.05, paste("q = ", format(x, nsmall=4, scientific=FALSE, zero.print=TRUE)), "" ) } ))
axis(4, pos=max(at)+0.15, at=ticks, lab=labs, lwd=0.5, col="gray90", las=2, cex.axis=1)
}
comparisonPlot = function(dataset, annotation, desc, column, columnb, funcname="diff", topmost=30, perc=TRUE, orderclasses=c(), legendtitle = paste(column), ordervars=c())
{
if (ncol(dataset)<5)
{
return (data.frame())
}
#print (dim(dataset))
tmp = prepareDataset(dataset)
func = get(funcname)
if (length(orderclasses)>0)
{
}
else
{
orderclasses = unique(annotation[,column])
}
######## normalize
if (perc)
{
for (k in 1:ncol(tmp))
{
### the dataset is per taxon level!
### i.e. NOT cummulative
if (sum(tmp[,k])==0)
{
}
else
{
tmp[,k] = tmp[,k]/sum(tmp[,k])
}
}
}
######### subset SampleIDs per group
x = aggregate(annotation[, c("SampleID")], list(annotation[,column ]), function(x){ return(levels(x)[x]) } )
y = aggregate(annotation[, c("SampleID")], list(annotation[,columnb]), function(x){ return(levels(x)[x]) } )
######## aggregate statistics
for (G in orderclasses)
{
# print (G)
groupA = x[x[,1]==G,]
membersA=unlist(groupA[,2])
#print (membersA)
# for func:
orderclassesB = sort(unique(annotation[,columnb]))
if (length(orderclassesB)==1)
{
return (data.frame())
}
for (H in orderclassesB)
{
groupB = y[y[,1]==H,]
membersB=unlist(groupB[,2])
members = membersA[membersA %in% membersB]
if (length(members)==0)
{
return (data.frame())
}
else if (length(members)>1)
{
tmp[,H] = apply( tmp[, members], 1, mean)
}
else
{
tmp[,H]=tmp[,members]
}
}
### !!!!!!!!!!!!!!!!!!!!!!!!!
### in case of diff, default, the order needs to be reversed, i.e. diff (1,2) = 1
tmp$new = apply(tmp[,rev(orderclassesB)],1, func)
names(tmp)[ncol(tmp)]<-c(G)
tmp = tmp[,!(names(tmp) %in% orderclassesB)]
}
######## keep only the aggregated statistics
if (is.null(nrow(tmp)))
{
return (data.frame())
}
tokeep = (ncol(tmp)-nrow(x)+1):ncol(tmp)
if (length(tokeep)>1)
{
tmp=tmp[,tokeep]
}
else
{
OO = data.frame(tmp[,tokeep], tmp[,tokeep])
names(OO) <- c(names(tmp)[tokeep], "removeme")
row.names(OO) <- row.names(tmp)
tmp=data.frame(OO)
}
######## order experiments (based on most differences between classes)
#print (tmp)
if (length(ordervars)==0)
{
fororder = apply(tmp, 1, ordering)
tmp = tmp[order(fororder, decreasing=TRUE),]
}
else
{
tmp = tmp[ordervars,]
}
#print (tmp)
#### plot only topX and accumulate the rest to form "Other"
if (topmost < nrow(tmp))
{
tmpA = tmp[1:(topmost+1) ,]
tmpB = tmp[(topmost+1):nrow(tmp),]
tmpA[topmost+1,] = apply(tmpB, 2, sum)
row.names(tmpA)[topmost+1] = paste("... [remaining" , nrow(tmpB),"]", sep=" ")
}
else
{
tmpA=tmp
}
### reverse order so that the MOST changed are at the top
tmpA = tmpA[rev(1:nrow(tmpA)),]
### flip so that data is grouped by taxa
tmpA = t(data.matrix(tmpA))
### reverse the order of classes so that it matches the ordercolumn from top to bottom
tmpA = tmpA[rev(1:nrow(tmpA)),]
#print (tmpA)
#cols = getColors(row.names(tmpA))
if (perc)
{
lab = paste(round(seq(min(tmp*100, -50),max(tmp*100, 50), length.out=11),0), "%")
at = seq(min(tmp, -0.5),max(tmp, 0.5), length.out=11)
}
else
{
lab = paste(round(seq(min(tmp, -150),max(tmp, 150), length.out=11),0))
at = seq(min(tmp, -150),max(tmp, 150), length.out=11)
}
#print(row.names(tmpA))
# print (dim(tmpA))
# tmpA=data.frame(tmpA[row.names(tmpA)!="removeme",])
# print (tmpA)
# print (dim(tmpA))
barx = barplot(tmpA,
main=paste(funcname, " ( ", paste(orderclassesB, collapse=" - "), " )\n", desc, sep=""),
legend = row.names(tmpA),
axes=FALSE,
cex.axis=0.8,
beside=TRUE,
horiz=TRUE,
las=1,
xlim = c(1.5*min(at),1.5*max(at)),
args.legend=list(x="right", cex=0.8, bg="ivory", title=legendtitle))
axis(1, at=at, lab=lab, las=3)
ticks = (1:ncol(tmpA))*(nrow(tmpA)+1) - nrow(tmpA)/2
axis(2, pos=1.5*min(at), at=ticks, lab=rep("",length(ticks)), lwd=5, col="gray80")
axis(2, pos=0 , at=ticks, lab=rep("",length(ticks)), lwd=5, col="gray80")
print (names(tmpA))
####repeat)
par(new=TRUE)
barx = barplot(tmpA,
main=paste(funcname, " ( ", paste(orderclassesB, collapse=" - "), " )\n", desc, sep=""),
legend = row.names(tmpA),
axes=FALSE,
cex.axis=0.8,
beside=TRUE,
horiz=TRUE,
las=1,
xlim = c(1.5*min(at),1.5*max(at)),
args.legend=list(x="right", cex=0.8, bg="ivory", title=legendtitle))
invisible (tmp)
}
error.bar <- function(x, y, upper, lower=upper, arlength=0.025, horiz=TRUE, ... ){
if(length(x) != length(y) | length(y) !=length(lower) | length(lower) != length(upper))
{
stop("vectors must be same length")
}
if (horiz)
{
arrows(y, x, y+upper,x, angle=90, code=2, length=arlength, ...)
}
else
{
arrows(x,y+upper, x, y-lower, angle=90, code=3, length=arlength, ...)
}
}
stackedBarPlot = function(dataset, annotation, desc, column, topmost=20, perc=TRUE, orderclasses=c())
{
tmp = prepareDataset(dataset)
if (length(orderclasses)>0)
{
}
else
{
orderclasses = unique(annotation[,column])
orderclasses = orderclasses[!is.na(orderclasses)]
#print (orderclasses)
}
#print (annotation[,column])
#print (orderclasses)
x = aggregate(annotation[, c("SampleID")], list(annotation[,column]), function(x){ return(levels(x)[x]) } )
tmp$sum = apply(tmp, 1, sum)
for (G in orderclasses)
{
group = x[x[,1]==G,]
members=unlist(group[,2])
if (length(members)>1)
{
tmp$new = apply( tmp[, names(tmp) %in% members], 1, mean)
}
else
{
tmp$new=tmp[, names(tmp) %in% members]
tmp = tmp[, ! (names(tmp) %in% members) ]
}
names(tmp)[ncol(tmp)]<-c(group[,1])
}
tmp = tmp[order(tmp$sum, decreasing=TRUE),]
#### plot only topX and accumulate the rest to form "Other"
if (topmost < nrow(tmp))
{
tmpA = tmp[1:(topmost+1) , (ncol(tmp)-nrow(x)):ncol(tmp)]
tmpB = tmp[(topmost+1):nrow(tmp), (ncol(tmp)-nrow(x)):ncol(tmp)]
tmpA[topmost+1,] = apply(tmpB, 2, sum)
row.names(tmpA)[topmost+1] = paste("... [remaining" , nrow(tmpB),"]", sep=" ")
}
else
{
tmpA=tmp[,(ncol(tmp)-nrow(x)):ncol(tmp)]
}
if (perc)
{
for (k in 2:ncol(tmpA))
{
tmpA[,k] = tmpA[,k]/sum(tmpA[,k])
}
lab = paste(seq(0,100, by=10), "%")
at = seq(0,1, by=0.1)
}
tmpA = tmpA[rev(1:nrow(tmpA)),]
cols = getColors(row.names(tmpA))
#print (max(tmpA[,-1]))