compute_SS_distances-methods: Compute distance between GO terms or GO clusters based on...

In abrionne/ViSEAGO: ViSEAGO: Easier data mining of biological functions organized into clusters using Gene Ontology and semantic similarity

Description

This method computes distance between GO terms or GO clusters based on semantic similarity.

Usage

compute_SS_distances(object, distance)

## S4 method for signature 'ANY,character'
compute_SS_distances(object, distance)

Arguments

object	a GO_SS-class, or GO_clusters-class objects created by build_GO_SS or GOterms_heatmap methods, respectively.
distance	The available methods for calculating GO terms Semantic Similarity (SS) are "Resnik", "Rel", "Lin", and "Jiang" which are based on Information Content (IC), and "Wang" which is based on graph topology. The available methods for calculating clusters of GO terms SS are "max", "avg","rcmax", and "BMA".

Details

This method computes semantic similarity distances between all GO terms provided by GO_SS-class object.
This method also computes semantic similarity distances between all GO clusters provided by GO_clusters-class object.

Semantic Similarity computations are based on mgoSim method from the GoSemSim package.

Value

a GO_SS-class, or a GO_clusters-class object (same class as input object).

References

Marc Carlson (2017). GO.db: A set of annotation maps describing the entire Gene Ontology. R package version 3.4.1.

Guangchuang Yu, Fei Li, Yide Qin, Xiaochen Bo, Yibo Wu and Shengqi Wang. GOSemSim: an R package for measuring semantic similarity among GO terms and gene products. Bioinformatics 2010 26(7):976-978

Herve Pages, Marc Carlson, Seth Falcon and Nianhua Li (2017). AnnotationDbi: Annotation Database Interface. R package version 1.38.0.

See Also

Other GO_semantic_similarity: GO_SS-class, build_GO_SS

Examples

###################
# load data example
data(
    myGOs,
    package="ViSEAGO"
)

## Not run: 
###################
# compute GO terms Semantic Similarity distances
myGOs<-ViSEAGO::compute_SS_distances(
    myGOs,
    distance=c("Resnik","Lin","Rel","Jiang","Wang")
)

##################
# GOtermsHeatmap with default parameters
Wang_clusters_wardD2<-ViSEAGO::GOterms_heatmap(
    myGOs,
    showIC=TRUE,
    showGOlabels=TRUE,
    GO.tree=list(
        tree=list(
            distance="Wang",
            aggreg.method="ward.D2",
            rotate=NULL
        ),
        cut=list(
            dynamic=list(
                pamStage=TRUE,
                pamRespectsDendro=TRUE,
                deepSplit=2,
                minClusterSize=2
            )
        )
    ),
    samples.tree=NULL
)

###################
# compute clusters of GO terms Semantic Similarity distances
Wang_clusters_wardD2<-ViSEAGO::compute_SS_distances(
    Wang_clusters_wardD2,
    distance=c("max","avg","rcmax","BMA")
)

## End(Not run)

abrionne/ViSEAGO documentation built on June 13, 2019, 2:27 p.m.