selectPrototypes {GOSim} | R Documentation |
selectPrototypes(n = 250, method = "frequency", data = NULL, verbose = TRUE)
n |
number of prototypes or maximum number of clusters |
method |
method to select prototypes or to perform subset selection |
data |
data matrix (l x d) of feature vectors (l = number of genes) |
verbose |
print out information |
The following heuristics to perform automatic selection of prototypes are implemented:
To perfom dimensionality reduction implemented methods are:
If the function is called to automatically select prototypes, a character vector of gene IDs is returned.
If the function is called to perform dimensionality via PCA, the result is a list with items
If the function is called to perform clustering in feature space, the
cluster centers are returned in a l x k matrix (each column is one
cluster center). The "Mclust" function in the package "mclust" is called
to perform the clustering. The BIC is used to calculate the optimal
number of clusters in the range 2,...,n.
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), pp. 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
, getGeneSimPrototypes
, setOntology
## Not run: # takes too much time in the R CMD check proto=selectPrototypes(n=50) # --> returns a character vector of 50 genes with the highest number of annotations feat=getGeneFeaturesPrototypes(c("207","208","7494"),prototypes=proto,pca=FALSE) # --> compute feature vectors selectPrototypes(data=feat$features,method="pca") # ... and PCA projection ## End(Not run)