hierMde: Function to do hierarchical cluster analysis

Description

This is a function to do hierarchical clustering analysis for objects of classe maigesDEcluster.

Usage

hierMde(data, group=c("C", "R", "B")[1], distance="correlation", method="complete", doHeat=TRUE, sLabelID="SAMPLE", gLabelID="GeneName", idxTest=1, adjP="BH", nDEgenes=0.05, ...)

Arguments

data

object of class maigesDEcluster.

group

character string giving the type of grouping: by rows 'R', columns 'C' (default) or both 'B'.

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'

doHeat

logical indicating to do or not the heatmap. If FALSE, only the dendrogram is displayed.

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 heatmap function.

Value

This function display the heatmaps and don't return any object or value.

Details

This function implements the hierarchical clustering method for objects resulted from differential expression analysis. The default function for hierarchical clustering is the hclust. For the adjustment of p-values in the selection of genes differentially expressed, we use the function mt.rawp2adjp from package multtest.

Examples

Run this code

## 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")

## Hierarchical cluster adjusting p-values by FDR, and showing all genes
## with p-value < 0.05
hierMde(gastro.ttest, sLabelID="Type", adjP="BH", nDEgenes=0.05)