getGeneSim {GOSim} | R Documentation |
Calculate the pairwise functional similarities for a list of genes using different strategies.
getGeneSim(genelist, similarity="funSimMax", similarityTerm="Lin", normalization=TRUE, method="sqrt", avg=(similarity=="OA"), verbose=TRUE)
genelist |
character vector of Entrez gene IDs |
similarity |
method to calculate the functional similarity between gene products |
similarityTerm |
method to compute the similarity of GO terms |
normalization |
normalize similarities yes/no |
method |
"sqrt": normalize sim(x,y) <- sim(x,y)/sqrt(sim(x,x)*sim(y,y)); "Lin": normalize sim(x,y) <- 2*sim(x,y)/(sim(x,x) + sim(y,y)); "Tanimoto": normalize sim(x,y) <- sim(x,y)/(sim(x,x) + sim(y,y) - sim(x,y)). NOTE: normalization does not have any effect, if similarity = "funSimMax", "funSimAvg" or similarity = "OA" and avg=TRUE |
avg |
standardize the OA kernel by the maximum number of GO terms for both genes |
verbose |
print out some information |
The method to calculate the pairwise functional similarity between gene products can either be:
n x n similarity matrix (n = number of genes)
The result depends on the currently set ontology.
Holger Froehlich
[1] H. Froehlich, N. Speer, C. Spieth, A. Zell, Kernel Based Functional Gene Grouping, Proc. Int. Joint Conf. on Neural Networks (IJCNN), 6886 - 6891, 2006.
[2] 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.
[3] A. del Pozo, F. Pazos, A. Valencia, Defining functional distances over Gene Ontology, BMC Bioinformatics, 9:50, 2008.
[4] M. Mistry, P Pavlidis, Gene Ontology term overlap as a measure of gene functional similarity, BMC Bioinformatics, 9:327, 2008.
getGeneSimPrototypes
, getTermSim
, setOntology
# see evaluateClustering