getGeneFeaturesPrototypes {GOSim} | R Documentation |
Computes feature vectors for list of genes: Each gene is represented by its similarities to predefined prototype genes.
getGeneFeaturesPrototypes(genelist, prototypes = NULL, similarity = "max", similarityTerm = "JiangConrath", pca = TRUE, normalization = TRUE, verbose = FALSE)
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 |
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
.
List with items
"features" |
feature vectors for each gene: n x d data matrix |
"prototypes" |
prototypes (= prinicipal components, if PCA has been performed) |
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
getGeneSimPrototypes
,
selectPrototypes
, getGeneSim
,
getTermSim
, setOntology
# see selectPrototypes