compute_SS_distances {ViSEAGO} | R Documentation

## Compute distance between GO terms or GO clusters based on 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)

[Package ViSEAGO version 1.6.0 Index]