GOclusters_heatmap-methods: Build a clustering heatmap on GO groups.

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

Description

This method computes a clustering heatmap based on GO groups semantic similarity.

Usage

GOclusters_heatmap(object, tree = list(distance = "BMA", aggreg.method =
  "ward.D2", rotate = NULL))

## S4 method for signature 'GO_clusters,list'
GOclusters_heatmap(object,
  tree = list(distance = "BMA", aggreg.method = "ward.D2", rotate =
  NULL))

Arguments

object

a GO_clusters-class object from compute_SS_distances.

tree

a named list with: distance ("BMA" by default) distance computed from the semantic similarity for GO groups which could be "max", "avg", "rcmax",or "BMA". aggreg.method ("ward.D2" by default) aggregation method criteria from hclust (ward.D", "ward.D2", "single", "complete", "average", "mcquitty", "median", or "centroid") to build a dendrogram. rotate sort the branches of the tree based on a vector - eithor of labels order or the labels in their new order

Details

This method computes a clustering heatmap based on GO groups semantic similarity (computed with compute_SS_distances).
The heatmap color intensity corresponds to the number of GO terms in each GO group.
GO group description is defined as the first common GO ancestor with the cluster identifiant in brackets.
The dendrogram branches are colored according to GO terms clusters.

Value

a GO_clusters-class object.

References

Matt Dowle and Arun Srinivasan (2017). data.table: Extension of 'data.frame'. R package version 1.10.4. https://CRAN.R-project.org/package=data.table.

Tal Galili (2015). dendextend: an R package for visualizing, adjusting, and comparing trees of hierarchical clustering. Bioinformatics. DOI:10.1093/bioinformatics/btv428.

Tal Galili (2017). heatmaply: Interactive Cluster Heat Maps Using 'plotly'. R package version 0.9.1. https://CRAN.R-project.org/package=heatmaply.

Erich Neuwirth (2014). RColorBrewer: ColorBrewer Palettes. R package version 1.1-2. https://CRAN.R-project.org/package=RColorBrewer.

Carson Sievert, Chris Parmer, Toby Hocking, Scott Chamberlain, Karthik Ram, Marianne Corvellec and Pedro Despouy (2017). plotly: Create Interactive Web Graphics via 'plotly.js'. R package version 4.6.0. https://CRAN.R-project.org/package=plotly.

H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2009.

See Also

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

Other semantic_similiarity: GOterms_heatmap

Other visualization: GOcount, GOterms_heatmap, Upset, available_organisms, overLapper, show_heatmap, show_table

Examples

###################
# load data example
utils::data(
    myGOs,
    package="ViSEAGO"
)
## Not run: 
###################
# compute GO terms Semantic Similarity distances
myGOs<-ViSEAGO::compute_SS_distances(
    myGOs,
    distance="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="BMA"
)

###################
# GOclusters heatmap
Wang_clusters_wardD2<-ViSEAGO::GOclusters_heatmap(
    Wang_clusters_wardD2,
    tree=list(
        distance="BMA",
        aggreg.method="ward.D2",
        rotate=NULL
    )
)

## End(Not run)

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