| clustering.plot {EMA} | R Documentation |
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.
EMA group
data(marty) ##Clustering on most variant genes mv.genes<-genes.selection(marty, thres.num=100) 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)