getGeneSimPrototypes {GOSim} | R Documentation

## Compute functional similarity of genes with respect to a feature vector representation.

### Description

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.

### Usage

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

### Arguments

`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

### Details

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.

### Value

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

### 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), 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`, `selectPrototypes`, `getGeneSim`, `getTermSim`, `setOntology`

### Examples

    #\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")
    #}

[Package GOSim version 1.26.0 Index]