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GOSim (version 1.10.0)

Computation of functional similarities between GO terms and gene products; GO enrichment analysis

Description

This package implements several functions useful for computing similarities between GO terms and gene products based on their GO annotation. Moreover it allows for computing a GO enrichment analysis

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Version

Monthly Downloads

137

Version

1.10.0

License

GPL (>= 2)

Maintainer

Holger Froehlich

Last Published

February 15th, 2017

Functions in GOSim (1.10.0)

getParents

Get direct parents for each GO term.
getChildren

Get a list of all direct children of each GO term.
GOenrichment

GO enrichment analysis
filterGO

Filter GO.
calc.diffusion.kernel

Calculation and loading of diffusion kernel matrices
IC

Information content of GO terms
setOntology

Set an ontology as base for subsequent computations.
getGeneFeatures

Get simple feature vector representation of genes
getGeneSimPrototypes

Compute functional similarity of genes with respect to a feature vector representation.
getGOInfo

Obtain GO terms and their description for a list of genes.
clusterEvaluation

Evaluate a given grouping of genes or GO terms.
getGeneFeaturesPrototypes

Get feature vector representation of genes relative to prototype genes
getMinimumSubsumer

Compute minimum subsumer of two GO terms.
getGOGraph

(1) Get GO graph with specified GO terms at its leave; (2) Get GO Graph with GO terms at leaves associated to one or several genes of interest.
calcICs

Calculate information contents of GO terms.
getDisjCommAnc

Get disjoint common ancestors.
setEvidenceLevel

Specifies to use only GO terms with given evidence codes.
getOffsprings

Get all offspring associated with one or more GO term
getTermSim

Get pairwise GO term similarities.
internal

internal functions
getAncestors

get list of ALL ancestors associated to each GO term
setEnrichmentFactors

Set the depth and densitiy enrichment factors for GO term similarity.
getGeneSim

Compute functional similarity for genes
selectPrototypes

Heuristic selection of prototypes and dimensionality reduction of feature vectors.