getTermSim {GOSim} | R Documentation

## Get pairwise GO term similarities.

### Description

Returns the pairwise similarities between GO terms. Different calculation method are implemented.

### Usage

    getTermSim(termlist, method = "relevance", verbose = FALSE)

### Arguments

`termlist` | character vector of GO terms
`method` | one of the supported methods for GO term similarity (see below)
`verbose` | print out various information or not

### Details

Currently the following methods for computing GO term similarities are implemented:

"Resnik"

  information content of minimum subsumer (ICms) [1], here additionally divided by the maximum information content of all GO terms

"JiangConrath"

  1 - \min(1, IC(term1) - 2ICms + IC(term2)) [2]

"Lin"

  \frac{2ICms}{(IC(term1) + IC(term2))} [3]

"CoutoEnriched"

  FuSSiMeg enriched term similarity by Couto et al. [4]. Requires enrichement factors to be set by `setEnrichmentFactors`

.

"CoutoResnik"

  average information content of common disjunctive ancestors of `term1` and `term2` (ICshare) [5]

"CoutoJiangConrath"

  1 - \min(1, IC(term1) -2ICshare + IC(term2)) [5]

"CoutoLin"

  \frac{2ICshare}{(IC(term1) + IC(term2))} [5]

"diffKernel"

  diffusion kernel similarity from a pre-loaded kernel matrix (see `load.diffusion.kernel`). The diffusion kernel is calculated using one of the methods described in [6].

"relevance"

  sim_Lin * (1 - exp(-ICms)) [7]

"GIC"

  summed information content of common ancestors divided by summed information content of all ancestors of term1 and term2 [8]

### Value

n x n matrix (n = number of GO terms) with similarities between GO terms scaled to [0,1]. If a GO term does not exist for the currently set ontology, the similarity is set to "NA".

### Note

All calculations use normalized information contents for each GO term. Normalization is achieved by dividing each information content by the maximum information content within the currently set ontology ("BP","MF","CC")

### Author(s)

Holger Froehlich

### References

[1] P. Resnik, Using Information Content to evaluate semantic similarity in a taxonomy, Proc. 14th Int. Conf. Artificial Intel., 1995

[2] J. Jiang, D. Conrath, Semantic Similarity based on Corpus Statistics and Lexical Taxonomy, Proc. Int. Conf. Research in Comp. Ling., 1998

[3] D. Lin, An Information-Theoretic Definition of Similarity, Proc. 15th Int. Conf. Machine Learning, 1998

[4] F. Couto, M. Silva, P. Coutinho, Implementation of a Functional Semantic Similarity Measure between Gene-Products, DI/FCUL TR 03-29, Department of Informatics, University of Lisbon, 2003

[5] Couto, F.; Silva, M. & Coutinho, P., Semantic Similarity over the Gene Ontology: Family Correlation and Selecting Disjunctive Ancestors, Conference in Information and Knowledge Management, 2005

[6] Lerman G. & Shaknovich B., Defining Functional Distance using Manifold Embeddings of Gene Ontology Annotations, PNAS, 104(27): 11334 - 11339, 2007

[7] A. Schlicker, F. Domingues, J. Rahnenfuehrer, T. Lengauer, A new measure for functional similarity of gene products based on Gene Ontology, BMC Bioinformatics, 7, 302, 2006.

[8] C. Pesquita, D. Faria, H. Bastos, A. Falcao, F. Couto, Evaluating GO-based Semantic Similarity Measures, In: Proc. 10th Annual Bio-Ontologies Meeting 2007, 37 - 40, 2007

### See Also

`getMinimumSubsumer`, `getDisjCommAnc`, `setEnrichmentFactors`, `setOntology`, `load.diffusion.kernel`

### Examples

    #\donttest{
     setOntology("BP")
     # Lin's method
     getTermSim(c("GO:0006955","GO:0007584"),method="Lin")
    # Couto's method combined with Jiang-Conrath distance
    getTermSim(c("GO:0006955","GO:0007584"),method="CoutoJiangConrath")

    # set enrichment factors
    setEnrichmentFactors(alpha=0.1,beta=0.5)
    getTermSim(c("GO:0006955","GO:0007584"),method="CoutoEnriched")
    #}

[Package GOSim version 1.28.0 Index]