GOSim (version 1.10.0)

getGeneFeaturesPrototypes: Get feature vector representation of genes relative to prototype genes

Description

Computes feature vectors for list of genes: Each gene is represented by its similarities to predefined prototype genes.

Usage

getGeneFeaturesPrototypes(genelist, prototypes = NULL, similarity = "max", similarityTerm = "JiangConrath", pca = TRUE, normalization = TRUE, verbose = FALSE)

Arguments

genelist

character vector of Entrez gene IDs

prototypes

character vector of Entrez gene IDs representing the prototypes

similarity

method to calculate the similarity to prototypes

similarityTerm

method to compute the GO term similarity

pca

perform PCA on feature vectors to reduce dimensionality

normalization

scale the feature vectors to norm 1

verbose

print out additional information

Value

List with items

"features"

feature vectors for each gene: n x d data matrix

"prototypes"

prototypes (= prinicipal components, if PCA has been performed)

Details

If no prototypes are passed, the method calls the selectPrototypes function with no arguments. Hence, the prototypes in this case are the 250 genes with most known annotations.

The PCA postprocessing determines the principal components that can explain at least 95% of the total variance in the feature space.

The method to calculate the functional similarity of a gene to a certain prototype can be any of those described in getGeneSim.

References

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

getGeneSimPrototypes, selectPrototypes, getGeneSim, getTermSim, setOntology

Examples

	# see selectPrototypes