GOclusters_heatmap {ViSEAGO} | R Documentation |
This method computes a clustering heatmap based on GO groups semantic similarity.
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) )
object |
a |
tree |
a named list with:
|
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.
a GO_clusters-class
object.
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.
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()
# 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)