heatmap

From stats v3.6.2 by R-core R-core@R-project.org

Draw a Heat Map

A heat map is a false color image (basically image(t(x))) with a dendrogram added to the left side and 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.

Keywords: hplot

Usage

heatmap(x, Rowv = NULL, Colv = if(symm)"Rowv" else NULL,
        distfun = dist, hclustfun = hclust,
        reorderfun = function(d, w) reorder(d, w),
        add.expr, symm = FALSE, revC = identical(Colv, "Rowv"),
        scale = c("row", "column", "none"), na.rm = TRUE,
        margins = c(5, 5), ColSideColors, RowSideColors,
        cexRow = 0.2 + 1/log10(nr), cexCol = 0.2 + 1/log10(nc),
        labRow = NULL, labCol = NULL, main = NULL,
        xlab = NULL, ylab = NULL,
        keep.dendro = FALSE, verbose = getOption("verbose"), …)

Arguments

x: numeric matrix of the values to be plotted.
Rowv: determines if and how the row dendrogram should be computed and reordered. Either a dendrogram or a vector of values used to reorder the row dendrogram or NA to suppress any row dendrogram (and reordering) or by default, NULL, see ‘Details’ below.
Colv: determines if and how the column dendrogram should be reordered. Has the same 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 (and so if there is to be no row dendrogram there will not be a column one either).
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. Should take as argument a result of distfun and return an object to which as.dendrogram can be applied.
reorderfun: function(d, w) of dendrogram and weights for reordering the row and column dendrograms. The default uses reorder.dendrogram.
add.expr: expression that will be evaluated after the call to image. Can be used to add components to the plot.
symm: logical indicating if x should be treated symmetrically; can only be true when x is a square matrix.
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.
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.
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.
main, xlab, ylab: main, x- and y-axis titles; defaults to none.
keep.dendro: logical indicating if the dendrogram(s) should be kept as part of the result (when Rowv and/or Colv are not NA).
verbose: logical indicating if information should be printed.
…: additional arguments passed on to image, e.g., col specifying the colors.

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 NA, no reordering will be done for the corresponding side.

By default (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 are not pretty. Consider using enhancements such as the RColorBrewer package.

Value

Invisibly, a list with components

rowInd

row index permutation vector as returned by order.dendrogram.

colInd

column index permutation vector.

Rowv

the row dendrogram; only if input Rowv was not NA and keep.dendro is true.

Colv

the column dendrogram; only if input Colv was not NA and keep.dendro is true.

Note

Unless Rowv = NA (or Colw = NA), 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() 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.

Examples

library(stats)

# NOT RUN {
require(graphics); require(grDevices)
x  <- as.matrix(mtcars)
rc <- rainbow(nrow(x), start = 0, end = .3)
cc <- rainbow(ncol(x), start = 0, end = .3)
hv <- heatmap(x, col = cm.colors(256), scale = "column",
              RowSideColors = rc, ColSideColors = cc, margins = c(5,10),
              xlab = "specification variables", ylab =  "Car Models",
              main = "heatmap(<Mtcars data>, ..., scale = \"column\")")
utils::str(hv) # the two re-ordering index vectors

## no column dendrogram (nor reordering) at all:
heatmap(x, Colv = NA, col = cm.colors(256), scale = "column",
        RowSideColors = rc, margins = c(5,10),
        xlab = "specification variables", ylab =  "Car Models",
        main = "heatmap(<Mtcars data>, ..., scale = \"column\")")
# }
# NOT RUN {
## "no nothing"
heatmap(x, Rowv = NA, Colv = NA, scale = "column",
        main = "heatmap(*, NA, NA) ~= image(t(x))")
# }
# NOT RUN {
<!-- %% also want example using the `add.exp' argument! -->
# }
# NOT RUN {
round(Ca <- cor(attitude), 2)
symnum(Ca) # simple graphic
heatmap(Ca,               symm = TRUE, margins = c(6,6)) # with reorder()
heatmap(Ca, Rowv = FALSE, symm = TRUE, margins = c(6,6)) # _NO_ reorder()
## slightly artificial with color bar, without and with ordering:
cc <- rainbow(nrow(Ca))
heatmap(Ca, Rowv = FALSE, symm = TRUE, RowSideColors = cc, ColSideColors = cc,
	margins = c(6,6))
heatmap(Ca,		symm = TRUE, RowSideColors = cc, ColSideColors = cc,
	margins = c(6,6))

## For variable clustering, rather use distance based on cor():
symnum( cU <- cor(USJudgeRatings) )

hU <- heatmap(cU, Rowv = FALSE, symm = TRUE, col = topo.colors(16),
             distfun = function(c) as.dist(1 - c), keep.dendro = TRUE)
## The Correlation matrix with same reordering:
round(100 * cU[hU[[1]], hU[[2]]])
## The column dendrogram:
utils::str(hU$Colv)
# }

Documentation reproduced from package stats, version 3.6.2, License: Part of R 3.6.2

Community examples

Glennlee at Jan 20, 2017 stats v3.2.1

require(graphics); require(grDevices) x <- as.matrix(mtcars) rc <- rainbow(nrow(x), start = 0, end = .3) cc <- rainbow(ncol(x), start = 0, end = .3) hv <- heatmap(x, col = cm.colors(256), scale = "column", RowSihttps://www.rdocumentation.org/deColors = rc, ColSideColors = cc, margins = c(5,10), xlab = "specification variables", ylab = "Car Models", main = "heatmap(<Mtcars data>, ..., scale = \"column\")") utils::str(hv) # the two re-ordering index vectors ## no column dendrogram (nor reordering) at all: heatmap(x, Colv = NA, col = cm.colors(256), scale = "column", RowSideColors = rc, margins = c(5,10), xlab = "specification variables", ylab = "Car Models", main = "heatmap(<Mtcars data>, ..., scale = \"column\")") ## "no nothing" heatmap(x, Rowv = NA, Colv = NA, scale = "column", main = "heatmap(*, NA, NA) ~= image(t(x))") round(Ca <- cor(attitude), 2) symnum(Ca) # simple graphic heatmap(Ca, symm = TRUE, margins = c(6,6)) # with reorder() heatmap(Ca, Rowv = FALSE, symm = TRUE, margins = c(6,6)) # _NO_ reorder() ## slightly artificial with color bar, without and with ordering: cc <- rainbow(nrow(Ca)) heatmap(Ca, Rowv = FALSE, symm = TRUE, RowSideColors = cc, ColSideColors = cc, margins = c(6,6)) heatmap(Ca, symm = TRUE, RowSideColors = cc, ColSideColors = cc, margins = c(6,6)) ## For variable clustering, rather use distance based on cor(): symnum( cU <- cor(USJudgeRatings) ) hU <- heatmap(cU, Rowv = FALSE, symm = TRUE, col = topo.colors(16), distfun = function(c) as.dist(1 - c), keep.dendro = TRUE) ## The Correlation matrix with same reordering: round(100 * cU[hU[[1]], hU[[2]]]) ## The column dendrogram: utils::str(hU$Colv)