| BHI {clValid} | R Documentation |
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 |
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. |
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.
Value
Returns the BHI measure as a numeric value.
Note
The main function for cluster validation is clValid, and
users should call this function directly if possible.
Author(s)
Guy Brock, Vasyl Pihur, Susmita Datta, Somnath Datta
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
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")
}