heatmap.3: Enhanced heatmap based on gplots::heatmap.2
In lwaldron/LeviRmisc: Levi's miscellaneous handy R functions

Description Usage Arguments Details Value Note Author(s) See Also Examples

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.

For now the only difference between gplots::heatmap.2 is the addition of the col.srt argument, which allows you to rotate column labels.

 heatmap.3 (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,
           col.srt = NULL,

           # 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 `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 `rev`ersed 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.
`col.srt`	If a numeric value, the number of degrees by which to rotate the column labels. Default is NULL for vertical labels.

`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`

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 `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

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.3() 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 heatmap.2, Levi Waldron heatmap.3.

image, 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.3(x)  ## default - dendrogram plotted and reordering done. 
 heatmap.3(x, dendrogram="none") ##  no dendrogram plotted, but reordering done.
 heatmap.3(x, dendrogram="row") ## row dendrogram plotted and row reordering done.
 heatmap.3(x, dendrogram="col") ## col dendrogram plotted and col reordering done.

 heatmap.3(x, keysize=2)  ## default - dendrogram plotted and reordering done.

 heatmap.3(x, Rowv=FALSE, dendrogram="both") ## generate warning!
 heatmap.3(x, Rowv=NULL, dendrogram="both")  ## generate warning!
 heatmap.3(x, Colv=FALSE, dendrogram="both") ## generate warning!

 ## 
 ## Show effect of z-score scaling within columns, blue-red color scale
 ##
 hv <- heatmap.3(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.3(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.3(Ca, 		 symm=TRUE, margin=c(6, 6), trace="none" )

 # without reorder
 heatmap.3(Ca, Rowv=FALSE, symm=TRUE, margin=c(6, 6), trace="none" )

 ## Place the color key below the image plot
 heatmap.3(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.3(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.3(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.3(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.3(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.3(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)