clustering.plot: Clustering plots for one or two ways representation
In EMA: Easy Microarray Data Analysis

Description Usage Arguments Details Author(s) See Also Examples

Creates plots for a clustering analysis.

clustering.plot(tree, tree.sup, data, lab, lab.sup,
dendro=TRUE, dendro.sup=TRUE, title="", scale="row", heatcol,
names=TRUE, names.sup=TRUE, names.dist=TRUE,
trim.heatmap=1, palette="rainbow", legend=TRUE, legend.pos="topright", ...)

`tree`	an object of class 'agnes' representing the first clustering.
`tree.sup`	optional - an object of class 'agnes' representing the second clustering.
`data`	optional - expression data for the heatmap plot
`lab`	optional - a matrix or data.frame of labels for 'tree' (by columns)
`lab.sup`	optional - a matrix or data.frame of labels for 'tree.sup' (by columns)
`dendro`	display dendrogram of tree object - The default is TRUE
`dendro.sup`	display dendogram of tree.sup object - The default is TRUE
`title`	optional - title of the graphic
`scale`	optional - character indicating if the values should be centered and scaled in either the row direction (gene) or the column direction (sample),or none. The default is '"row"'
`heatcol`	colors for the heatmap generated by myPalette
`names`	optional - if names=FALSE, the labels for 'tree' are not written - The default is TRUE
`names.sup`	optional - if names.sup=FALSE, the labels for 'tree.sup' are not written - The default is TRUE
`names.dist`	Display the distance used for the Hierachical Clustering - The default is TRUE
`trim.heatmap`	Percentile of the data to be trimmed. This helps to keep an informative color scale in the heatmap
`palette`	Palette used for color selection. see as.colors()
`legend`	Draw legend of the labels. Default is TRUE
`legend.pos`	Position of the legend (topright, topleft, bottomright, bottomleft). Default is topright
`...`	Arguments to be passed to methods, such as graphical parameters (see 'par').

If the data matrix is specified, the function draws a clustering using the heatmap representation. If tree.sup is specified the function draws a two-ways clustering using the heatmap representation. Otherwise, a classical dendrogram is displayed. If a labels matrix is specified, each column of the matrix is represented under the dendrogram. If a pdfname is specified, the output is a pdf file. Setting 'trim.heatmap' to a number between 0 and 1 uses equidistant classes between the (trim.heatmap)- and (1-trim.heatmap)-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.

Nicolas Servant, Eleonore Gravier, Pierre Gestraud, Cecile Laurent, Caroline Paccard, Anne Biton, Jonas Mandel, Bernard Asselain, Emmanuel Barillot, Philippe Hupe

clustering, heatmap.plus

data(marty)

##Clustering on 50 most variant genes amongst 500 first 
mv.genes<-genes.selection(marty[1:500,], thres.num=50)
c.sample<-clustering(marty[mv.genes,], metric="pearson", metho="ward")
clustering.plot(c.sample, lab=marty.type.cl, title="H.Clustering\nPearson-Ward")

c.gene<-clustering(data=t(marty[mv.genes,]), metric="pearson",method="ward")

##Two-ways clustering
clustering.plot(tree=c.sample, tree.sup=c.gene, data=marty[mv.genes,], trim.heatmap=0.99)