# NOT RUN {
data(mtcars)
x <- as.matrix(mtcars)
rc <- rainbow(nrow(x), start=0, end=.3)
cc <- rainbow(ncol(x), start=0, end=.3)
##
## demonstrate the effect of row and column dendrogram options
##
heatmap.2(x) ## default - dendrogram plotted and reordering done.
heatmap.2(x, dendrogram="none") ## no dendrogram plotted, but reordering done.
heatmap.2(x, dendrogram="row") ## row dendrogram plotted and row reordering done.
heatmap.2(x, dendrogram="col") ## col dendrogram plotted and col reordering done.
heatmap.2(x, keysize=2) ## default - dendrogram plotted and reordering done.
heatmap.2(x, Rowv=FALSE, dendrogram="both") ## generates a warning!
heatmap.2(x, Rowv=NULL, dendrogram="both") ## generates a warning!
heatmap.2(x, Colv=FALSE, dendrogram="both") ## generates a warning!
## Reorder dendrogram by branch means rather than sums
heatmap.2(x, reorderfun=function(d, w) reorder(d, w, agglo.FUN = mean) )
## plot a sub-cluster using the same color coding as for the full heatmap
full <- heatmap.2(x)
heatmap.2(x, Colv=full$colDendrogram[[2]], breaks=full$breaks) # column subset
heatmap.2(x, Rowv=full$rowDendrogram[[1]], breaks=full$breaks) # row subset
heatmap.2(x, Colv=full$colDendrogram[[2]],
Rowv=full$rowDendrogram[[1]], breaks=full$breaks) # both
## Show effect of row and column label rotation
heatmap.2(x, srtCol=NULL)
heatmap.2(x, srtCol=0, adjCol = c(0.5,1) )
heatmap.2(x, srtCol=45, adjCol = c(1,1) )
heatmap.2(x, srtCol=135, adjCol = c(1,0) )
heatmap.2(x, srtCol=180, adjCol = c(0.5,0) )
heatmap.2(x, srtCol=225, adjCol = c(0,0) ) ## not very useful
heatmap.2(x, srtCol=270, adjCol = c(0,0.5) )
heatmap.2(x, srtCol=315, adjCol = c(0,1) )
heatmap.2(x, srtCol=360, adjCol = c(0.5,1) )
heatmap.2(x, srtRow=45, adjRow=c(0, 1) )
heatmap.2(x, srtRow=45, adjRow=c(0, 1), srtCol=45, adjCol=c(1,1) )
heatmap.2(x, srtRow=45, adjRow=c(0, 1), srtCol=270, adjCol=c(0,0.5) )
## Show effect of offsetRow/offsetCol (only works when srtRow/srtCol is
## not also present)
heatmap.2(x, offsetRow=0, offsetCol=0)
heatmap.2(x, offsetRow=1, offsetCol=1)
heatmap.2(x, offsetRow=2, offsetCol=2)
heatmap.2(x, offsetRow=-1, offsetCol=-1)
heatmap.2(x, srtRow=0, srtCol=90, offsetRow=0, offsetCol=0)
heatmap.2(x, srtRow=0, srtCol=90, offsetRow=1, offsetCol=1)
heatmap.2(x, srtRow=0, srtCol=90, offsetRow=2, offsetCol=2)
heatmap.2(x, srtRow=0, srtCol=90, offsetRow=-1, offsetCol=-1)
## Show how to use 'extrafun' to replace the 'key' with a scatterplot
lmat <- rbind( c(5,3,4), c(2,1,4) )
lhei <- c(1.5, 4)
lwid <- c(1.5, 4, 0.75)
myplot <- function() {
oldpar <- par("mar")
par(mar=c(5.1, 4.1, 0.5, 0.5))
plot(mpg ~ hp, data=x)
}
heatmap.2(x, lmat=lmat, lhei=lhei, lwid=lwid, key=FALSE, extrafun=myplot)
## show how to customize the color key
heatmap.2(x,
key.title=NA, # no title
key.xlab=NA, # no xlab
key.par=list(mgp=c(1.5, 0.5, 0),
mar=c(2.5, 2.5, 1, 0)),
key.xtickfun=function() {
breaks <- parent.frame()$breaks
return(list(
at=parent.frame()$scale01(c(breaks[1],
breaks[length(breaks)])),
labels=c(as.character(breaks[1]),
as.character(breaks[length(breaks)]))
))
})
heatmap.2(x,
breaks=256,
key.title=NA,
key.xlab=NA,
key.par=list(mgp=c(1.5, 0.5, 0),
mar=c(1, 2.5, 1, 0)),
key.xtickfun=function() {
cex <- par("cex")*par("cex.axis")
side <- 1
line <- 0
col <- par("col.axis")
font <- par("font.axis")
mtext("low", side=side, at=0, adj=0,
line=line, cex=cex, col=col, font=font)
mtext("high", side=side, at=1, adj=1,
line=line, cex=cex, col=col, font=font)
return(list(labels=FALSE, tick=FALSE))
})
##
## Show effect of z-score scaling within columns, blue-red color scale
##
hv <- heatmap.2(x, col=bluered, scale="column", tracecol="#303030")
###
## Look at the return values
###
names(hv)
## Show the mapping of z-score values to color bins
hv$colorTable
## Extract the range associated with white
hv$colorTable[hv$colorTable[,"color"]=="#FFFFFF",]
## Determine the original data values that map to white
whiteBin <- unlist(hv$colorTable[hv$colorTable[,"color"]=="#FFFFFF",1:2])
rbind(whiteBin[1] * hv$colSDs + hv$colMeans,
whiteBin[2] * hv$colSDs + hv$colMeans )
##
## A more decorative heatmap, with z-score scaling along columns
##
hv <- heatmap.2(x, col=cm.colors(255), scale="column",
RowSideColors=rc, ColSideColors=cc, margin=c(5, 10),
xlab="specification variables", ylab= "Car Models",
main="heatmap(<Mtcars data>, ..., scale=\"column\")",
tracecol="green", density="density")
## Note that the breakpoints are now symmetric about 0
## Color the labels to match RowSideColors and ColSideColors
hv <- heatmap.2(x, col=cm.colors(255), scale="column",
RowSideColors=rc, ColSideColors=cc, margin=c(5, 10),
xlab="specification variables", ylab= "Car Models",
main="heatmap(<Mtcars data>, ..., scale=\"column\")",
tracecol="green", density="density", colRow=rc, colCol=cc,
srtCol=45, adjCol=c(0.5,1))
# }
# NOT RUN {
<!-- %% want example using the `add.exp' argument! -->
# }
# NOT RUN {
data(attitude)
round(Ca <- cor(attitude), 2)
symnum(Ca) # simple graphic
# with reorder
heatmap.2(Ca, symm=TRUE, margin=c(6, 6), trace="none" )
# without reorder
heatmap.2(Ca, Rowv=FALSE, symm=TRUE, margin=c(6, 6), trace="none" )
## Place the color key below the image plot
heatmap.2(x, lmat=rbind( c(0, 3), c(2,1), c(0,4) ), lhei=c(1.5, 4, 2 ) )
## Place the color key to the top right of the image plot
heatmap.2(x, lmat=rbind( c(0, 3, 4), c(2,1,0 ) ), lwid=c(1.5, 4, 2 ) )
## For variable clustering, rather use distance based on cor():
data(USJudgeRatings)
symnum( cU <- cor(USJudgeRatings) )
hU <- heatmap.2(cU, Rowv=FALSE, symm=TRUE, col=topo.colors(16),
distfun=function(c) as.dist(1 - c), trace="none")
## The Correlation matrix with same reordering:
hM <- format(round(cU, 2))
hM
# now with the correlation matrix on the plot itself
heatmap.2(cU, Rowv=FALSE, symm=TRUE, col=rev(heat.colors(16)),
distfun=function(c) as.dist(1 - c), trace="none",
cellnote=hM)
## genechip data examples
# }
# NOT RUN {
library(affy)
data(SpikeIn)
pms <- SpikeIn@pm
# just the data, scaled across rows
heatmap.2(pms, col=rev(heat.colors(16)), main="SpikeIn@pm",
xlab="Relative Concentration", ylab="Probeset",
scale="row")
# fold change vs "12.50" sample
data <- pms / pms[, "12.50"]
data <- ifelse(data>1, data, -1/data)
heatmap.2(data, breaks=16, col=redgreen, tracecol="blue",
main="SpikeIn@pm Fold Changes\nrelative to 12.50 sample",
xlab="Relative Concentration", ylab="Probeset")
# }
# NOT RUN {
# }
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