clustheatmap {kmed}

Consensus matrix heatmap from A consensus matrix

Description

This function creates a consensus matrix heatmap from a consensus/ agreement matrix. The values of the consensus/ agreement matrix are transformed in order to plot the heatmap.

Usage

clustheatmap(consmat, title = "")

Arguments

consmat

A matrix of consensus/ agreement matrix (see Details).

title

A title of the plot.

Details

This is a function to produce a consensus matrix heatmap from a consensus/ agreement matrix. A matrix produced by the consensusmatrix function can be directly provided in the consmat argument. The values of the consensus matrix, A, are then transformed via a non-linear transformation by applying

a_{ij}^{trf} = \frac{a_{ij} - min(a_{..})}{max(a_{..}) - min(a_{..})}

where a_{ij} is the value of the consensus matrix in row i and column j, and a_{..} is the all values of the matrix (\forallA).

Value

Function returns a heatmap plot.

Author(s)

Weksi Budiaji
Contact: [e-mail]

References

Monti, S., P. Tamayo, J. Mesirov, and T. Golub. 2003. Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data. Machine Learning 52 pp. 91-118.

Hahsler, M., and Hornik, K., 2011. Dissimilarity plots: A visual exploration tool for partitional clustering. Journal of Computational and Graphical Statistics 20(2) pp. 335-354.

Examples

num <- as.matrix(iris[,1:4])
mrwdist <- distNumeric(num, num, method = "mrw")
irisfast <- clustboot(mrwdist, nclust=3, nboot=7)
complete <- function(x, nclust) {
res <- hclust(as.dist(x), method = "complete")
member <- cutree(res, nclust)
return(member)
}
consensuscomplete <- consensusmatrix(irisfast, nclust = 3, reorder = complete)
clustheatmap(consensuscomplete)