heatmap_plus: Display an Annotated Heatmap

Description

This function displays an expression data matrix as a heatmap with a column dendrogram. A given clustering will be shown in color. Additionally, a number of binary and interval scaled covariates can be added to characterize these clusters.

This function is just about to be deprecated. Please use functions annHeatmap or annHeatmap2 for new projects.

Usage

heatmap_plus(x, addvar, covariate = NULL, picket.control = list(), h, clus, cluscol, cluslabel = NULL, Rowv, Colv, reorder = c(TRUE, TRUE),
			 distfun = dist, hclustfun = hclust, scale = c("row", "column",
			 "none"), na.rm = TRUE, do.dendro = TRUE, col = heat.colors(12),
			 trim, equalize = FALSE, ...)

Arguments

the numerical data matrix to be displayed.

addvar

data frame with (mostly binary) covariates.

covariate

integer indicating the one column in addvar that is interval scaled.

picket.control

list of option for drawing the covariates, passed to oldPicketplot.

height at which to cut the dendrogram, as in oldCutree; overrides clus.

clus

an explicit vector of cluster memberships for the columns of x, if no dendrogram is used; ignored if do.dendro=TRUE and h is specified.

cluscol

a vector of colors used to indicate clusters.

cluslabel

labels to designate cluster names.

Rowv

either a dendrogram or a vector of reordering indexes for the rows.

Colv

either a dendrogram or a vector of reordering indexes for the columns.

reorder

logical vector of length two, indicating whether the rows and columns (in this order) should be reordered using order.dendrogram.

distfun

function to compute the distances between rows and columns. Defaults to dist.

hclustfun

function used to cluster rows and columns. Defaults to hclust.

scale

indicates whether values should be scaled by either by row, column, or not at all. Defaults to row.

na.rm

logical indicating whther to remove NAs.

do.dendro

logical indicating whether to draw the column dendrogram.

col

the color scheme for image. The default sucks.

trim

Percentage of values to be trimmed. This helps to keep an informative color scale, see Details.

equalize

logical indicating whther to use the ranks of the data for setting the color scheme; alternative to trim, see Details.

...

extra arguments to image.

Value

rowInd: indices of the rows of the display in terms of the rows of x.
colInd: ditto for the columns of the display.
clus: the cluster indices of the columns of the display.

Details

This is a heavily modified version of heatmap_2, which is a heavily modfied version of an old version of heatmap in package stats, so some of the arguments are described in more detail there. The main distinguishing feature of this routine is the possibility to color a cluster solution, and to add a covariate display.

Covariates are assumed to be binary, coded as 0 and 1 (or FALSE and TRUE respectively). One of the covariates can be interval scaled, the column index of this variable is supplied via argument covariate. The details of the added display are handled by the function picketplot. Setting trim to a number between 0 and 1 uses equidistant classes between the (trim)- and (1-trim)-quantile, and lumps the values below and above this range into separate open-ended classes. If the data comes from a heavy-tailed distribution, this can save the display from putting too many values into to few classes. Alternatively, you can set equal=TRUE, which uses an equidistant color scheme for the ranks of the values.

Examples

Run this code

# create data
mm = matrix(rnorm(1000, m=1), 100,10)
mm = cbind(mm, matrix(rnorm(2000), 100, 20))
mm = cbind(mm, matrix(rnorm(1500, m=-1), 100, 15))
mm2 = matrix(rnorm(450), 30, 15)
mm2 = cbind(mm2, matrix(rnorm(900,m=1.5), 30,30))
mm=rbind(mm, mm2)
colnames(mm) = paste("Sample", 1:45)
rownames(mm) = paste("Gene", 1:130)
addvar = data.frame(Var1=rep(c(0,1,0),c(10,20,15)),
                    Var2=rep(c(1,0,0),c(10,20,15)),
                    Var3=rep(c(1,0), c(15,30)),
                    Var4=rep(seq(0,1,length=4), c(10,5,15,15))+rnorm(45, sd=0.5))
addvar[3,3] = addvar[17,2] = addvar[34,1] =NA
colnames(addvar) = c("Variable X","Variable Y", "ZZ","Interval")


# the lame default, without clustering
# Labels do not look too hot that way
heatmap_plus(mm)

# without labels, but with cluster
dimnames(mm)=NULL
heatmap_plus(mm, h=40)

# add some covariates, with nice names
heatmap_plus(mm, addvar=addvar, cov=4)

# covariates and clustering
heatmap_plus(mm, addvar=addvar, cov=4, h=20, col=RGBColVec(64), equal=TRUE)

# Clustering without the dendrogram
cc = cutree(hclust(dist(t(mm))), k=5)
heatmap_plus(mm, addvar=addvar, cov=4, clus=cc, do.dendro=FALSE)

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