A heat map is a false color image (basically
image(t(x))) with a dendrogram added to the left side
and/or to the top. Typically, reordering of the rows and columns
according to some set of values (row or column means) within the
restrictions imposed by the dendrogram is carried out.
This heatmap provides a number of extensions to the standard R
heatmap function.
heatmap.2 (x, # dendrogram control
Rowv = TRUE,
Colv=if(symm)"Rowv" else TRUE,
distfun = dist,
hclustfun = hclust,
dendrogram = c("both","row","column","none"),
reorderfun = function(d, w) reorder(d, w),
symm = FALSE,
# data scaling
scale = c("none","row", "column"),
na.rm=TRUE,
# image plot
revC = identical(Colv, "Rowv"),
add.expr,
# mapping data to colors
breaks,
symbreaks=any(x < 0, na.rm=TRUE) || scale!="none",
# colors
col="heat.colors",
# block sepration
colsep,
rowsep,
sepcolor="white",
sepwidth=c(0.05,0.05),
# cell labeling
cellnote,
notecex=1.0,
notecol="cyan",
na.color=par("bg"),
# level trace
trace=c("column","row","both","none"),
tracecol="cyan",
hline=median(breaks),
vline=median(breaks),
linecol=tracecol,
# Row/Column Labeling
margins = c(5, 5),
ColSideColors,
RowSideColors,
cexRow = 0.2 + 1/log10(nr),
cexCol = 0.2 + 1/log10(nc),
labRow = NULL,
labCol = NULL,
srtRow = NULL,
srtCol = NULL,
adjRow = c(0,NA),
adjCol = c(NA,0),
offsetRow = 0.5,
offsetCol = 0.5,
colRow = NULL,
colCol = NULL,
# color key + density info
key = TRUE,
keysize = 1.5,
density.info=c("histogram","density","none"),
denscol=tracecol,
symkey = any(x < 0, na.rm=TRUE) || symbreaks,
densadj = 0.25,
key.title = NULL,
key.xlab = NULL,
key.ylab = NULL,
key.xtickfun = NULL,
key.ytickfun = NULL,
key.par=list(),
# plot labels
main = NULL,
xlab = NULL,
ylab = NULL,
# plot layout
lmat = NULL,
lhei = NULL,
lwid = NULL,
# extras
extrafun=NULL,
...
)
numeric matrix of the values to be plotted.
determines if and how the row dendrogram should be
reordered. By default, it is TRUE, which implies dendrogram is
computed and reordered based on row means. If NULL or FALSE, then no
dendrogram is computed and no reordering is done. If a
dendrogram, then it is used "as-is", ie
without any reordering. If a vector of integers, then dendrogram is
computed and reordered based on the order of the vector.
determines if and how the column dendrogram should
be reordered. Has the options as the Rowv argument above and
additionally when x is a square matrix,
Colv="Rowv" means that columns should be treated identically
to the rows.
function used to compute the distance (dissimilarity)
between both rows and columns. Defaults to dist.
function used to compute the hierarchical clustering
when Rowv or Colv are not dendrograms. Defaults to
hclust.
character string indicating whether to draw 'none', 'row', 'column' or 'both' dendrograms. Defaults to 'both'. However, if Rowv (or Colv) is FALSE or NULL and dendrogram is 'both', then a warning is issued and Rowv (or Colv) arguments are honoured.
function(d, w) of dendrogram and weights for
reordering the row and column dendrograms. The default uses
stats{reorder.dendrogram}
logical indicating if x should be treated
symmetrically; can only be true when x is a
square matrix.
character indicating if the values should be centered and
scaled in either the row direction or the column direction, or
none. The default is "none".
logical indicating whether NA's should be removed.
logical indicating if the column order should be
reversed for plotting, such that e.g., for the
symmetric case, the symmetry axis is as usual.
expression that will be evaluated after the call to
image. Can be used to add components to the plot.
(optional) Either a numeric vector indicating the
splitting points for binning x into colors, or a integer
number of break points to be used, in which case the break points
will be spaced equally between min(x) and max(x).
Boolean indicating whether breaks should be
made symmetric about 0. Defaults to TRUE if the data includes
negative values, and to FALSE otherwise.
colors used for the image. Defaults to heat colors
(heat.colors).
(optional) vector of integers
indicating which columns or rows should be separated from the
preceding columns or rows by a narrow space of color
sepcolor.
(optional) Vector of length 2 giving the width
(colsep) or height (rowsep) the separator box drawn by colsep and
rowsep as a function of the width (colsep) or height (rowsep) of a
cell. Defaults to c(0.05, 0.05)
(optional) matrix of character strings which will be placed within each color cell, e.g. p-value symbols.
(optional) numeric scaling factor for cellnote
items.
(optional) character string specifying the color for
cellnote text. Defaults to "cyan".
Color to use for missing value (NA). Defaults
to the plot background color.
character string indicating whether a solid "trace" line should be drawn across 'row's or down 'column's, 'both' or 'none'. The distance of the line from the center of each color-cell is proportional to the size of the measurement. Defaults to 'column'.
character string giving the color for "trace" line. Defaults to "cyan".
Vector of values within cells where a
horizontal or vertical dotted line should be drawn. The color of
the line is controlled by linecol. Horizontal lines are only
plotted if trace is 'row' or 'both'. Vertical lines are only
drawn if trace 'column' or 'both'. hline and
vline default to the median of the breaks, linecol
defaults to the value of tracecol.
numeric vector of length 2 containing the margins
(see par(mar= *)) for column and row names,
respectively.
(optional) character vector of length
ncol(x) containing the color names for a horizontal side bar
that may be used to annotate the columns of x.
(optional) character vector of length
nrow(x) containing the color names for a vertical side bar
that may be used to annotate the rows of x.
positive numbers, used as cex.axis in
for the row or column axis labeling. The defaults currently only
use number of rows or columns, respectively.
character vectors with row and column labels to
use; these default to rownames(x) or colnames(x),
respectively.
angle of row/column labels, in degrees from horizontal
2-element vector giving the (left-right, top-bottom) justification of row/column labels (relative to the text orientation).
Number of character-width spaces to place between row/column labels and the edge of the plotting region.
color of row/column labels, either a scalar to set the color of all labels the same, or a vector providing the colors of each label item
logical indicating whether a color-key should be shown.
numeric value indicating the size of the key
character string indicating whether to superimpose a 'histogram', a 'density' plot, or no plot ('none') on the color-key.
character string giving the color for the density
display specified by density.info, defaults to the same value
as tracecol.
Boolean indicating whether the color key should be
made symmetric about 0. Defaults to TRUE if the data includes
negative values, and to FALSE otherwise.
Numeric scaling value for tuning the kernel width when
a density plot is drawn on the color key. (See the adjust
parameter for the density function for details.) Defaults to
0.25.
main title of the color key. If set to NA no title will be plotted.
x axis label of the color key. If set to NA no label will be plotted.
y axis label of the color key. If set to NA no label will be plotted.
function computing tick location and labels for
the xaxis of the color key. Returns a named list containing
parameters that can be passed to axis. See examples.
function computing tick location and labels for
the y axis of the color key. Returns a named list containing
parameters that can be passed to axis. See examples.
graphical parameters for the color key. Named list that
can be passed to par.
main, x- and y-axis titles; defaults to none.
visual layout: position matrix, column height, column width. See below for details
A function to be called after all other work. See examples.
additional arguments passed on to image
Invisibly, a list with components
row index permutation vector as returned by
order.dendrogram.
column index permutation vector.
the matched call
mean and standard deviation of each row: only
present if scale="row"
mean and standard deviation of each column: only
present if scale="column"
reordered and scaled 'x' values used generate the main 'carpet'
row dendrogram, if present
column dendrogram, if present
values used for color break points
colors used
center-line value used for column trace, present only if
trace="both" or trace="column"
center-line value used for row trace, present only if
trace="both" or trace="row"
A three-column data frame providing the lower and upper bound and color for each bin
A named list containing the values used for
lmat, lhei, and lwid.
If either Rowv or Colv are dendrograms they are honored
(and not reordered). Otherwise, dendrograms are computed as
dd <- as.dendrogram(hclustfun(distfun(X))) where X is
either x or t(x).
If either is a vector (of “weights”) then the appropriate
dendrogram is reordered according to the supplied values subject to
the constraints imposed by the dendrogram, by reorder(dd,
Rowv), in the row case.
If either is missing, as by default, then the ordering of the
corresponding dendrogram is by the mean value of the rows/columns,
i.e., in the case of rows, Rowv <- rowMeans(x, na.rm=na.rm).
If either is NULL, no reordering will be done for
the corresponding side.
If scale="row" (or scale="col") the rows (columns) are
scaled to have mean zero and standard deviation one. There is some
empirical evidence from genomic plotting that this is useful.
The default colors range from red to white (heat.colors) and
are not pretty. Consider using enhancements such as the
RColorBrewer package,
https://cran.r-project.org/package=RColorBrewer
to select better colors.
By default four components will be displayed in the plot. At the top
left is the color key, top right is the column dendrogram, bottom left
is the row dendrogram, bottom right is the image plot. When
RowSideColor or ColSideColor are provided, an additional row or column
is inserted in the appropriate location. This layout can be
overriden by specifiying appropriate values for lmat,
lwid, and lhei. lmat controls the relative
postition of each element, while lwid controls the column
width, and lhei controls the row height. See the help page for
layout for details on how to use these
arguments.
# 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 {
# }
Run the code above in your browser using DataCamp Workspace