HeatmapMethods {GeneSelector} | R Documentation

## Heatmap of genes and ranking procedures

### Description

Cluster genes and ranking procedures simultanesously based on a data matrix of ranks whose columns correspond to ranking procedures and whose rows correspond to genes. The main goal is to compare different ranking procedures and to examine whether there are big differences among them. Up to now, the (totally unweighted) euclidean metric and complete-linkage clustering is used to generate the trees. It should be mentionned that this method only fulfills an exploratory task.

### Usage

    HeatmapMethods(Rlist, ind = 1:100)

### Arguments

`Rlist` | A list of objects of class GeneRanking or AggregatedRanking.
`ind` | A vector of gene indices whose ranks are used to generate the heatmap. The number of elements should not be too large (not greater than 500) due high time and memory requirements.

### Value

A heatmap (plot).

### Author(s)

Martin Slawski martin.slawski@campus.lmu.de
Anne-Laure Boulesteix http://www.slcmsr.net/boulesteix

### References

Gentleman, R., Carey, V.J, Huber, W., Irizarry, R.A, Dudoit, S. (editors), 2005.
Bioinformatics and Computational Biology Solutions Using R and Bioconductor chapter 10, Visualizing Data. Springer, N.Y.

### Examples

    ## Load toy gene expression data
    data(toydata)
    ### class labels
    yy <- toydata[1,]
    ### gene expression
    xx <- toydata[-1,]
    ### Get Rankings from five different statistics
    ordinaryT <- RankingTstat(xx, yy, type="unpaired")
    baldilongT <- RankingBaldiLong(xx, yy, type="unpaired")
    samT <- RankingSam(xx, yy, type="unpaired")
    wilc <- RankingWilcoxon(xx, yy, type="unpaired")
    wilcebam <- RankingWilcEbam(xx, yy, type="unpaired")
    ### form a list
    LL <- list(ordinaryT, baldilongT, samT, wilc, wilcebam)
    ### plot the heatmap
    HeatmapMethods(LL, ind=1:100)

[Package GeneSelector version 1.4.0 Index]