heatmap.2

From gplots v3.0.1 by the of this package were written by Gregory R. Warnes

Enhanced Heat Map

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.

Keywords: hplot

Usage

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, ... )

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.
reorderfun: function(d, w) of dendrogram and weights for reordering the row and column dendrograms. The default uses stats{reorder.dendrogram}
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 "none".
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 "cyan".
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.
colRow, colCol: 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
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.
key.title: main title of the color key. If set to NA no title will be plotted.
key.xlab: x axis label of the color key. If set to NA no label will be plotted.
key.ylab: y axis label of the color key. If set to NA no label will be plotted.
key.xtickfun: 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.
key.ytickfun: 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.
key.par: graphical parameters for the color key. Named list that can be passed to par.
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
extrafun: A function to be called after all other work. See examples.
...: 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" (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.

Value

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
layout: A named list containing the values used for lmat, lhei, and lwid.

Note

The original rows and columns are reordered to match the dendrograms Rowv and Colv (if present).

heatmap.2() uses layout to arragent the plot elements. Consequentially, it can not be used in a multi column/row layout using layout(...), par(mfrow=...) or (mfcol=...).

Examples

 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))





 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)

Documentation reproduced from package gplots, version 3.0.1, License: GPL-2

Community examples

peggy40429@gmail.com at Dec 20, 2017 gplots v3.0.1

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)) 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)

heatmap.2

Enhanced Heat Map

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Enhanced Heat Map

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