clValid (version 0.7)

BHI: Biological Homogeneity Index

Description

Calculates the biological homogeneity index (BHI) for a given statistical clustering partition and biological annotation.

Usage

BHI(statClust, annotation, names = NULL, category = "all", dropEvidence=NULL)

Arguments

statClust

An integer vector indicating the statistical cluster partitioning

annotation

Either a character string naming the Bioconductor annotation package for mapping genes to GO categories, or a matrix where each column is a logical vector indicating which genes belong to the biological functional class. See details below.

names

A vector of labels to associate with the 'genes', to be used in conjunction with the Bioconductor annotation package. Not needed if annotation is a list providing the functional classes.

category

Indicates the GO categories to use for biological validation. Can be one of "BP", "MF", "CC", or "all".

dropEvidence

Which GO evidence codes to omit. Either NULL or a character vector, see 'Details' below.

Value

Returns the BHI measure as a numeric value.

Details

The BHI measures how homogeneous the clusters are biologically. The measure checks whether genes placed in the same statistical cluster also belong to the same functional classes. The BHI is in the range [0,1], with larger values corresponding to more biologically homogeneous clusters. For details see the package vignette.

When inputting the biological annotation and functional classes directly, the BSI function expects the input in ``matrix'' format, where each column is a logical vector indicating which genes belong to the biological class. For details on how to input the biological annotation from an Excel file see readAnnotationFile and for converting from list to matrix format see annotationListToMatrix.

The dropEvidence argument indicates which GO evidence codes to omit. For example, "IEA" is a relatively weak association based only on electronic information, and users may wish to omit this evidence when determining the functional annotation classes.

References

Datta, S. and Datta, S. (2006). Methods for evaluating clustering algorithms for gene expression data using a reference set of functional classes. BMC Bioinformatics 7:397.

See Also

For a description of the function 'clValid' see clValid.

For a description of the class 'clValid' and all available methods see clValidObj or clValid-class.

For additional help on the other validation measures see connectivity, dunn, stability, and BSI.

Examples

# NOT RUN {
data(mouse)
express <- mouse[1:25,c("M1","M2","M3","NC1","NC2","NC3")]
rownames(express) <- mouse$ID[1:25]
## hierarchical clustering
Dist <- dist(express,method="euclidean")
clusterObj <- hclust(Dist, method="average")
nc <- 4 ## number of clusters      
cluster <- cutree(clusterObj,nc)

## first way - functional classes predetermined
fc <- tapply(rownames(express),mouse$FC[1:25], c)
fc <- fc[-match( c("EST","Unknown"), names(fc))]
fc <- annotationListToMatrix(fc, rownames(express))
BHI(cluster, fc)

## second way - using Bioconductor
if(require("Biobase") && require("annotate") && require("GO.db") &&
require("moe430a.db")) {
  BHI(cluster, annotation="moe430a.db", names=rownames(express), category="all")
}

# }