kmeansMde {maigesPack} | R Documentation

## Function to do k-means cluster analysis

### Description

This is a function to do k-means clustering analysis for objects of class `maigesDEcluster`.

### Usage

    kmeansMde(data, group=c("C", "R")[1], distance="correlation",
              method="complete", sampleT=NULL, doHier=FALSE, sLabelID="SAMPLE",
              gLabelID="GeneName", idxTest=1, adjP="none", nDEgenes=0.05, ...)

### Arguments

`data` | object of class `maigesDEcluster`.
`group` | character string giving the type of grouping: by rows 'R' or columns 'C' (default).
`distance` | char string giving the type of distance to use. Here we use the function `Dist` and the possible values are 'euclidean', 'maximum', 'manhattan', 'canberra', 'binary', 'pearson', 'correlation' (default) and 'spearman'.
`method` | char string specifying the linkage method for the hierarchical cluster. Possible values are 'ward', 'single', 'complete' (default), 'average', 'mcquitty', 'median' or 'centroid'
`sampleT` | list with 2 vectors. The first one specify the first letter of different sample types to be coloured by distinct colours, that are given in the second vector. If NULL (default) no colour is used.
`doHier` | logical indicating if you want to do the hierarchical branch in the opposite dimension of clustering. Defaults to FALSE.
`sLabelID` | character string specifying the sample label ID to be used to label the samples.
`gLabelID` | character string specifying the gene label ID to be used to label the genes.
`idxTest` | numerical index of the test to be used to sort the genes when clustering objects of class `maigesDEcluster`.
`adjP` | string specifying the method of p-value adjustment. May be 'none', 'Bonferroni', 'Holm', 'Hochberg', 'SidakSS', 'SidakSD', 'BH', 'BY'.
`nDEgenes` | number of DE genes to be selected. If a real number in (0,1) all genes with p.value <= `nDEgenes` will be used. If an integer, the `nDEgenes` genes with smaller p-values will be used.
`...` | additional parameters for `Kmeans` function.

### Details

This function implements the k-means clustering method for objects resulted from differential analysis. The method uses the function `Kmeans` from package amap. For the adjustment of p-values in the selection of genes differentially expressed, we use the function `mt.rawp2adjp` from package multtest.

### Value

This function display the heatmaps and return invisibly a list resulted from the function `Kmeans`.

### Author(s)

Gustavo H. Esteves <gesteves@vision.ime.usp.br>

### See Also

`Kmeans` from package amap. `mt.rawp2adjp` from package multtest. `somM` and `hierM` for displaying SOM and hierarchical clusters, respectively.

### Examples

    ## Loading the dataset
    data(gastro)

    ## Doing bootstrap from t statistic test fot 'Type' sample label, k=1000
    ## specifies one thousand bootstraps
    gastro.ttest = deGenes2by2Ttest(gastro.summ, sLabelID="Type")

    ## K-means cluster with 2 groups adjusting p-values by FDR, and showing all genes
    ## with p-value < 0.05
    kmeansMde(gastro.ttest, sLabelID="Type", adjP="BH", nDEgenes=0.05, centers=2)

    ## K-means cluster with 3 groups adjusting p-values by FDR, and showing all genes
    ## with p-value < 0.05
    kmeansMde(gastro.ttest, sLabelID="Type", adjP="BH", nDEgenes=0.05, centers=3)

    dev.off()

[Package maigesPack version 1.51.0 Index]