heatmap.2

heatmap.2 {gplots}

R Documentation

Enhanced Heat Map

Description

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.

Usage

heatmap.2 (x,

           # dendrogram control
           Rowv = TRUE,
           Colv=if(symm)"Rowv" else TRUE,
           distfun = dist,
           hclustfun = hclust,
           dendrogram = c("both","row","column","none"),
           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=min(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,

           # color key + density info
           key = TRUE,
           keysize = 1.5,
           density.info=c("histogram","density","none"),
           denscol=tracecol,
           symkey = min(x < 0, na.rm=TRUE) || symbreaks,
           densadj = 0.25,

           # plot labels
           main = NULL,
           xlab = NULL,
           ylab = NULL,

           # plot layout
           lmat = NULL,
           lhei = NULL,
           lwid = NULL,

           # extras
           ...
           )
           

Arguments

x	numeric matrix of the values to be plotted.
Rowv	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.
Colv	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.
distfun	function used to compute the distance (dissimilarity) between both rows and columns. Defaults to dist.
hclustfun	function used to compute the hierarchical clustering when Rowv or Colv are not dendrograms. Defaults to hclust.
dendrogram	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.
symm	logical indicating if x should be treated symmetrically; can only be true when x is a square matrix.

scale	character indicating if the values should be centered and scaled in either the row direction or the column direction, or none. The default is "row" if symm false, and "none" otherwise.
na.rm	logical indicating whether NA's should be removed.

revC	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.
add.expr	expression that will be evaluated after the call to image. Can be used to add components to the plot.
breaks	(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).
symbreaks	Boolean indicating whether breaks should be made symmetric about 0. Defaults to TRUE if the data includes negative values, and to FALSE otherwise.
col	colors used for the image. Defaults to heat colors (heat.colors).

colsep, rowsep, sepcolor	(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.
sepwidth	(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)

cellnote	(optional) matrix of character strings which will be placed within each color cell, e.g. p-value symbols.
notecex	(optional) numeric scaling factor for cellnote items.
notecol	(optional) character string specifying the color for cellnote text. Defaults to "green".
na.color	Color to use for missing value (NA). Defaults to the plot background color.

trace	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'.
tracecol	character string giving the color for "trace" line. Defaults to "cyan".
hline, vline, linecol	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.

margins	numeric vector of length 2 containing the margins (see par(mar= *)) for column and row names, respectively.
ColSideColors	(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.
RowSideColors	(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.
cexRow, cexCol	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.
labRow, labCol	character vectors with row and column labels to use; these default to rownames(x) or colnames(x), respectively.
srtRow, srtCol	angle of row/column labels, in degrees from horizontal
adjRow, adjCol	2-element vector giving the (left-right, top-bottom) justification of row/column labels (relative to the text orientation).
offsetRow, offsetCol	Number of character-width spaces to place between row/column labels and the edge of the plotting region.

key	logical indicating whether a color-key should be shown.
keysize	numeric value indicating the size of the key
density.info	character string indicating whether to superimpose a 'histogram', a 'density' plot, or no plot ('none') on the color-key.
denscol	character string giving the color for the density display specified by density.info, defaults to the same value as tracecol.
symkey	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.
densadj	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, xlab, ylab

main, x- and y-axis titles; defaults to none.

lmat, lhei, lwid	visual layout: position matrix, column height, column width. See below for details
...	additional arguments passed on to image

Details

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" the rows 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,
http://cran.r-project.org/src/contrib/PACKAGES.html#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 dendogram, bottom left
is the row dendogram, 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.

Value

Invisibly, a list with components

rowInd	row index permutation vector as returned by order.dendrogram.
colInd	column index permutation vector.
call	the matched call
rowMeans, rowSDs	mean and standard deviation of each row: only present if scale="row"
colMeans, colSDs	mean and standard deviation of each column: only present if scale="column"
carpet	reordered and scaled 'x' values used generate the main 'carpet'
rowDendrogram	row dendrogram, if present
colDendrogram	column dendrogram, if present
breaks	values used for color break points
col	colors used
vline	center-line value used for column trace, present only if trace="both" or trace="column"
hline	center-line value used for row trace, present only if trace="both" or trace="row"
colorTable	A three-column data frame providing the lower and upper bound and color for each bin

Note

The original rows and columns are reordered in any case to
match the dendrogram, e.g., the rows by
order.dendrogram(Rowv) where Rowv is the
(possibly reorder()ed) row dendrogram.

heatmap.2() uses layout and draws the
image in the lower right corner of a 2x2 layout.
Consequentially, it can not be used in a multi column/row
layout, i.e., when par(mfrow= *) or (mfcol= *)
has been called.

Author(s)

Andy Liaw, original; R. Gentleman, M. Maechler, W. Huber,
G. Warnes, revisions.

See Also

hclust

Examples

 library(gplots)
 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 dendogram 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") ## generate warning!
 heatmap.2(x, Rowv=NULL, dendrogram="both")  ## generate warning!
 heatmap.2(x, Colv=FALSE, dendrogram="both") ## generate warning!

 ## 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 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

 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")

## End(Not run)

---

[Package gplots version 2.12.1 Index]

 

ref:

http://hosho.ees.hokudai.ac.jp/~kubo/Rdoc/library/gplots/html/heatmap.2.html

 

http://bbsunchen.iteye.com/blog/1271580