GOSim (version 1.10.0)

selectPrototypes: Heuristic selection of prototypes and dimensionality reduction of feature vectors.

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

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.

Details

The following heuristics to perform automatic selection of prototypes are implemented: [object Object],[object Object] To perfom dimensionality reduction implemented methods are: [object Object],[object Object]

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