heatmap.2 {gplots} | R Documentation |
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"), 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 ... )
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 |
Colv |
determines if and how the column dendrogram should be reordered. Has the options as the |
distfun |
function used to compute the distance (dissimilarity) between both rows and columns. Defaults to |
hclustfun |
function used to compute the hierarchical clustering when |
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 |
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 |
na.rm |
logical indicating whether |
revC |
logical indicating if the column order should be |
add.expr |
expression that will be evaluated after the call to |
breaks |
(optional) Either a numeric vector indicating the splitting points for binning |
symbreaks |
Boolean indicating whether breaks should be made symmetric about 0. Defaults to |
col |
colors used for the image. Defaults to 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 |
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 |
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 |
notecol |
(optional) character string specifying the color for |
na.color |
Color to use for missing value ( |
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 |
margins |
numeric vector of length 2 containing the margins (see |
ColSideColors |
(optional) character vector of length |
RowSideColors |
(optional) character vector of length |
cexRow, cexCol |
positive numbers, used as |
labRow, labCol |
character vectors with row and column labels to use; these default to |
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 |
symkey |
Boolean indicating whether the color key should be made symmetric about 0. Defaults to |
densadj |
Numeric scaling value for tuning the kernel width when a density plot is drawn on the color key. (See the |
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 |
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.
Invisibly, a list with components
rowInd |
row index permutation vector as returned by |
colInd |
column index permutation vector. |
call |
the matched call |
rowMeans, rowSDs |
mean and standard deviation of each row: only present if |
colMeans, colSDs |
mean and standard deviation of each column: only present if |
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 |
hline |
center-line value used for row trace, present only if |
colorTable |
A three-column data frame providing the lower and upper bound and color for each bin |
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.
Andy Liaw, original; R. Gentleman, M. Maechler, W. Huber, G. Warnes, revisions.
hclust
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)