clusters_cor-methods: Compute distance matrix between dendrograms partitions.

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

Description

Build a distance or correlation matrix between partitions from dendrograms.

Usage

clusters_cor(clusters, method = "adjusted.rand")

## S4 method for signature 'list,character'
clusters_cor(clusters,
  method = "adjusted.rand")

Arguments

clusters	a list of GO_clusters-class objects, from GOterms_heatmap or GOclusters_heatmap, named as character.
method	available methods ("vi", "nmi", "split.join", "rand", or "adjusted.rand") from igraph package compare function.

Value

a distance or a correlation matrix.

References

Csardi G, Nepusz T: The igraph software package for complex network research, InterJournal, Complex Systems 1695. 2006. http://igraph.org.

See Also

Other GO_clusters: GO_clusters-class, GOclusters_heatmap, compare_clusters, show_heatmap, show_table

Examples

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

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

##################
# Resnik distance GO terms heatmap
Resnik_clusters_wardD2<-ViSEAGO::GOterms_heatmap(
    myGOs,
    showIC=TRUE,
    showGOlabels=TRUE,
    GO.tree=list(
        tree=list(
            distance="Resnik",
            aggreg.method="ward.D2"
        ),
        cut=list(
            dynamic=list(
                deepSplit=2,
                minClusterSize =2
            )
        )
    ),
    samples.tree=NULL
)

##################
# Lin distance GO terms heatmap
Lin_clusters_wardD2<-ViSEAGO::GOterms_heatmap(
    myGOs,
    showIC=TRUE,
    showGOlabels=TRUE,
    GO.tree=list(
        tree=list(
            distance="Lin",
            aggreg.method="ward.D2"
        ),
        cut=list(
            dynamic=list(
                deepSplit=2,
                minClusterSize =2
            )
        )
    ),
    samples.tree=NULL
)

##################
# Resnik distance GO terms heatmap
Rel_clusters_wardD2<-ViSEAGO::GOterms_heatmap(
    myGOs,
    showIC=TRUE,
    showGOlabels=TRUE,
    GO.tree=list(
        tree=list(
            distance="Rel",
            aggreg.method="ward.D2"
        ),
        cut=list(
            dynamic=list(
                deepSplit=2,
                minClusterSize =2
            )
        )
    ),
    samples.tree=NULL
)

##################
# Resnik distance GO terms heatmap
Jiang_clusters_wardD2<-ViSEAGO::GOterms_heatmap(
    myGOs,
    showIC=TRUE,
    showGOlabels=TRUE,
    GO.tree=list(
        tree=list(
            distance="Jiang",
            aggreg.method="ward.D2"
        ),
        cut=list(
            dynamic=list(
                deepSplit=2,
                minClusterSize =2
            )
        )
    ),
    samples.tree=NULL
)

##################
# Resnik distance GO terms heatmap
Wang_clusters_wardD2<-ViSEAGO::GOterms_heatmap(
    myGOs,
    showIC=TRUE,
    showGOlabels=TRUE,
    GO.tree=list(
        tree=list(
            distance="Wang",
            aggreg.method="ward.D2"
        ),
        cut=list(
            dynamic=list(
                deepSplit=2,
                minClusterSize =2
            )
        )
    ),
    samples.tree=NULL
)

## End(Not run)
###################
# clusters to compare
clusters<-list(
    Resnik="Resnik_clusters_wardD2",
    Lin="Lin_clusters_wardD2",
    Rel="Rel_clusters_wardD2",
    Jiang="Jiang_clusters_wardD2",
    Wang="Wang_clusters_wardD2"
)

## Not run: 
###################
# global dendrogram clustering correlation
clust_cor<-ViSEAGO::clusters_cor(
    clusters,
    method="adjusted.rand"
)

## End(Not run)

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