heatmap_plus: Display an Annotated Heatmap

In alexploner/Heatplus: Heatmaps with row and/or column covariates and colored clusters

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.

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

x	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.
h	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.

Details

This function is deprecated. Please use functions annHeatmap or annHeatmap2 for new projects.

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.

Value

A list with components

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.

Author(s)

Original by Andy Liaw, with revisions by Robert Gentleman and Martin Maechler.

Alexander Ploner for the modifications documented here.

See Also

heatmap_2, heatmap, oldPicketplot, oldCutplot.dendrogram, RGBColVec, annHeatmap, annHeatmap2

Examples

## Not run:  
# 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)

## End(Not run) ## end dontrun

alexploner/Heatplus documentation built on May 23, 2021, 2:01 p.m.