bio3d (version 2.4-4)

hclustplot: Dendrogram with Clustering Annotation

Description

Draw a standard dendrogram with clustering annotation in the marginal regions and colored labels.

Usage

hclustplot(hc, k = NULL, h = NULL, colors = NULL, labels = NULL,
             fillbox = FALSE, heights = c(1, .3), mar = c(1, 1, 0, 1), ...)

Value

Called for its effect.

Arguments

hc

an object of the type produced by hclust.

k

an integer scalar or vector with the desired number of groups. Redirected to function cutree.

h

numeric scalar or vector with heights where the tree should be cut. Redirected to function cutree. At least one of ‘k’ or ‘h’ must be specified.

colors

a numerical or character vector with the same length as ‘hc’ specifying the colors of the labels.

labels

a character vector with the same length as ‘hc’ containing the labels to be written.

fillbox

logical, if TRUE clustering annotation will be drawn as filled boxes below the dendrogram.

heights

numeric vector of length two specifying the values for the heights of rows on the device. See function layout.

mar

a numerical vector of the form ‘c(bottom, left, top, right)’ which gives the number of lines of margin to be specified on the four sides of the plot. If left at default the margins will be adjusted upon adding arguments ‘main’, ‘ylab’, etc.

...

other graphical parameters passed to functions plot.dendrogram, mtext, and par. Note that certain arguments will be ignored.

Author

Lars Skjaerven

Details

This function adds extended visualization of cluster membership to a standard dendrogram. If ‘k’ or ‘h’ is provided a call to cutree will provide cluster membership information. Alternatively a vector of colors or cluster membership information can be provided through argument ‘colors’.

See examples for further details on usage.

References

Grant, B.J. et al. (2006) Bioinformatics 22, 2695--2696.

See Also

plot.hclust, plot.dendrogram, hclust, cutree.

Examples

# \donttest{
# Redundant testing excluded

attach(transducin)

##- perform RMSD clustering
rd <- rmsd(pdbs, fit=TRUE)
hc <- hclust(as.dist(rd))

##- draw dendrogram 
hclustplot(hc, k=3)

##- draw dendrogram with manual clustering annotation
#hclustplot(hc, colors=annotation[, "color"], labels=pdbs$id)

detach(transducin)

# }