| heatplot {made4} | R Documentation |
heatplot calls heatmap using a red-green colour scheme by default. It also draws dendrograms of the cases and variables
using correlation similarity metric and average linkage clustering as described by Eisen. heatplot is useful for a
quick overview or exploratory analysis of data
heatplot(dataset, dend = TRUE, lowcol = "green",
highcol = "red", Colv=NULL, Rowv=NULL, ...)
dataset |
a matrix, data.frame,
exprSet or marrayRaw.
If the input is gene expression data in a matrix or data.frame. The
rows and columns are expected to contain the variables (genes) and cases (array samples)
respectively. |
dend |
Logical, indicating whether dendrograms should be drawn. Default is TRUE. If FALSE both Colv and Rowv are set to NA. |
Colv, Rowv |
Vector or object of class dendrogram used to reorder the columns, or rows. If no ordering is
required, set Colv or Rowv = NA. The default is NULL |
lowcol, highcol |
Character indicating colours to be used for down and upregulated genes when drawing heatmap. Default is lowcol="green", and highcol="red". |
... |
further arguments passed to or from other methods |
The hierarchical plot is produced using average linkage cluster analysis with a
correlation metric distance. heatplot calls heatmap and
dendrogram.
Because Eisen et al., 1998 use green-red colours for the heatmap heatplot
uses these by default however a blue-red or yellow-blue are easily obtained by
changing lowcol and highcol
Aedin Culhane
Eisen MB, Spellman PT, Brown PO and Botstein D. (1998). Cluster Analysis and Display of Genome-Wide Expression Patterns. Proc Natl Acad Sci USA 95, 14863-8.
See also as hclust,
heatmap and dendrogram
data(khan)
heatplot(khan$train[1:30,], lowcol="blue", highcol="red")
heatplot(khan$train[1:26,], lowcol="blue", highcol="red",
labRow = c(64:1), labCol=LETTERS[1:26])
if (require(ade4, quiet = TRUE)) {
# To speed up analysis, only a subset of the genes are analysed
khan.sub<-array2ade4(khan$train[1:1000,])
khan.coa<-dudi.coa(khan.sub, scan=FALSE, nf=6)
}
# Provides a view of the components of the Correspondence analysis
heatplot(khan.coa$li, dend=FALSE)
# transposed so that it is easier to view. Can see that the difference between tissues
# and cell line samples are defined in the first axis.
heatplot(t(khan.coa$li), dend=FALSE, lowcol="blue")