getGeneSimPrototypes: Compute functional similarity of genes with respect to a...
In GOSim: Computation of functional similarities between GO terms and gene products; GO enrichment analysis

Description Usage Arguments Details Value Note Author(s) References See Also Examples

Computes the pairwise functional similarities for a list of genes: Each gene is represented by a feature vector containing the gene's similarities to predefined prototype genes.

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

`genelist`	character vector of primary gene IDs according to organism annotation package (see `setEvidenceLevel`)
`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`	normalize similarities to [0,1]: sim(x,y) <- 0.5(sim(x,y)/sqrt(sim(x,x)sim(y,y)) + 1)
`verbose`	print additional information

The method calls getGeneFeaturesPrototypes to calculate the feature vectors. The functional similarity between two genes is essentially given by the dot product between their feature vectors.

List with items

`"similarity"`	n x n similarity matrix (n = number of genes)
`"prototypes"`	prototypes (= prinicipal components, if PCA has been performed)
`"features"`	feature vectors for each gene: n x d data matrix

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

Holger Froehlich

[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

getGeneFeaturesPrototypes, selectPrototypes, getGeneSim, getTermSim, setOntology

#\donttest{ may take some time ...
 proto=selectPrototypes(n=5) # --> returns a character vector of 5 genes with the highest number of annotations
 getGeneSimPrototypes(c("207","208"),prototypes=proto, similarityTerm="Resnik")
#}