kmeansMde: Function to do k-means cluster analysis

In maigesPack: Functions to handle cDNA microarray data, including several methods of data 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 <[e-mail]>

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()

maigesPack documentation built on Nov. 8, 2020, 6:23 p.m.