selectPrototypes: Heuristic selection of prototypes and dimensionality...

In GOSim: Computation of functional similarities between GO terms and gene products; GO enrichment analysis

Description

• Heuristic selection of prototypes

• Dimensionality reduction of feature vectors

Usage

selectPrototypes(n = 250, method = "frequency", data = NULL, verbose = FALSE)

Arguments

n	number of prototypes or maximum number of clusters
method	method to select prototypes or to perform subset selection
data	data matrix (l x d) of feature vectors (l = number of genes)
verbose	print out information

Details

The following heuristics to perform automatic selection of prototypes are implemented:

"frequency"

select n genes with highest number of GO annotations in the currently selected ontology

"random"

select n genes uniform randomly over all genes with annotations in the currently selected ontology

To perfom dimensionality reduction implemented methods are:

"PCA"

dimensionality reduction via principal component analysis; the number of principal components is determined such that at least 95% of total variance in feature space can be explained

"clustering"

EM-clustering in feature space

Value

If the function is called to automatically select prototypes, a character vector of gene IDs is returned.

If the function is called to perform dimensionality via PCA, the result is a list with items

If the function is called to perform clustering in feature space, the cluster centers are returned in a l x k matrix (each column is one cluster center). The "flexmix" function in the package "flexmix" is called to perform the clustering. The BIC is used to calculate the optimal number of clusters in the range 2,...,n.

Note

The result depends on the currently set ontology ("BP","MF","CC").

Author(s)

Holger Froehlich

References

[1] H. Froehlich, N. Speer, C. Spieth, and A. Zell, Kernel Based Functional Gene Grouping, Proc. Int. Joint Conf. on Neural Networks (IJCNN), pp. 6886 - 6891, 2006

[2] N. Speer, H. Froehlich, A. Zell, Functional Grouping of Genes Using Spectral Clustering and Gene Ontology, Proc. Int. Joint Conf. on Neural Networks (IJCNN), pp. 298 - 303, 2005

See Also

getGeneFeaturesPrototypes, getGeneSimPrototypes, setOntology

Examples

 # takes too much time in the R CMD check
 proto=selectPrototypes(n=5) # --> returns a character vector of 5 genes with the highest number of annotations 
 feat=getGeneFeaturesPrototypes(c("207","7494"),prototypes=proto,pca=FALSE) # --> compute feature vectors 
 selectPrototypes(data=feat$features,method="pca") # ... and PCA projection

GOSim documentation built on Nov. 8, 2020, 11:05 p.m.