kmeansM {maigesPack} | R Documentation

## Function to do k-means cluster analysis

### Description

This is a function to do k-means clustering analysis for objects of classes `maiges`, `maigesRaw` and `maigesANOVA`. Use the function `kmeansMde` for objects of class `maigesDEcluster`.

### Usage

    kmeansM(data, group=c("C", "R")[1], distance="correlation",
            method="complete", sampleT=NULL, doHier=FALSE, sLabelID="SAMPLE",
            gLabelID="GeneName", rmGenes=NULL, rmSamples=NULL, rmBad=TRUE,
            geneGrp=NULL, path=NULL, ...)

### Arguments

`data` | object of class `maigesRaw`, `maiges` or `maigesANOVA`.
`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.
`rmGenes` | char list specifying genes to be removed.
`rmSamples` | char list specifying samples to be removed.
`rmBad` | logical indicating to remove or not bad spots (slot `BadSpots` in objects of class `maiges`, `maigesRaw` or `maigesANOVA`).
`geneGrp` | numerical or character specifying the gene group to be clustered. This is given by the columns of the slot `GeneGrps` in objects of classes `maiges`, `maigesRaw` and `maigesANOVA`.
`path` | numerical or character specifying the gene network to be clustered. This is given by the items of the slot `Paths` in objects of classes `maiges`, `maigesRaw` and `maigesANOVA`.
`...` | additional parameters for `Kmeans` function.

### Details

This function implements the k-means clustering method for objects of microarray data defined in this package. The method uses the function `Kmeans` from package amap.

### 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. `somM` and `hierM` for displaying SOM and hierarchical clusters, respectively.

### Examples

    ## Loading the dataset
    data(gastro)

    ## Doing a K-means cluster with 2 groups using all genes, for maigesRaw class
    kmeansM(gastro.raw, rmGenes=c("BLANK","DAP","LYS","PHE", "Q_GENE","THR","TRP"),
            sLabelID="Sample", gLabelID="Name", centers=2)

    ## The same as above, but for maigesNorm class
    kmeansM(gastro.norm, rmGenes=c("BLANK","DAP","LYS","PHE", "Q_GENE","THR","TRP"),
            sLabelID="Sample", gLabelID="Name", centers=2)

    ## Another example with 3 groups
    kmeansM(gastro.norm, rmGenes=c("BLANK","DAP","LYS","PHE", "Q_GENE","THR","TRP"),
            sLabelID="Sample", gLabelID="Name", centers=3)

    ## If you want to use euclidean distance to group genes (or spots) with
    ## 4 groups
    kmeansM(gastro.summ, rmGenes=c("BLANK","DAP","LYS","PHE", "Q_GENE","THR","TRP"),
            sLabelID="Sample", gLabelID="Name", centers=4, group="R", distance="euclidean")

[Package maigesPack version 1.8.0 Index]