selectPrototypes {GOSim} | R Documentation

## Heuristic selection of prototypes and dimensionality reduction of feature vectors.

### Description

- Heuristic selection of prototypes

- Dimensionality reduction of feature vectors

### Usage

    selectPrototypes(n = 250, method = "frequency", data = NULL, verbose = FALSE)

### Arguments

`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

### Details

The following heuristics to perform automatic selection of prototypes are implemented:

"frequency"

  select n genes with highest number of GO annotations in the currently selected ontology

"random"

  select n genes uniform randomly over all genes with annotations in the currently selected ontology

To perfom dimensionality reduction implemented methods are:

"PCA"

  dimensionality reduction via principal component analysis; the number of principal components is determined such that at least 95% of total variance in feature space can be explained

"clustering"

  EM-clustering in feature space

### Value

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 "flexmix" function in the package "flexmix" is called to perform the clustering. The BIC is used to calculate the optimal number of clusters in the range 2,...,n.

### 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), 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

### See Also

`getGeneFeaturesPrototypes`, `getGeneSimPrototypes`, `setOntology`

### Examples

     # takes too much time in the R CMD check
     proto=selectPrototypes(n=5) # --> returns a character vector of 5 genes with the highest number of annotations
     feat=getGeneFeaturesPrototypes(c("207","7494"),prototypes=proto,pca=FALSE) # --> compute feature vectors
     selectPrototypes(data=feat$features,method="pca") # ... and PCA projection

[Package GOSim version 1.28.0 Index]