In Bioconductor 3.19, ontoProc can work with OWL RDF/XML serializations of ontologies, via the owlready2 python modules.
The owl2cache
function retrieves OWL from a URL or file
and places it in a cache to avoid repetitious retrievals. The
default cache is the one defined by BiocFileCache::BiocFileCache()
.
Here we work with the cell ontology. setup_entities2
will use basilisk
to acquire
owlready2 python modules that parse the OWL and produce an ontology_index
instance
(defined in CRAN package ontologyIndex).
library(ontoProc)
clont_path = owl2cache(url="http://purl.obolibrary.org/obo/cl.owl")
cle = setup_entities2(clont_path)
cle
## Ontology with 16430 terms
##
## Properties:
## id: character
## name: character
## parents: list
## children: list
## ancestors: list
## obsolete: logical
## Roots:
## BFO_0000003 - NA
## BFO_0000002 - NA
## BFO_0000004 - NA
## GO_0050878 - regulation of body fluid levels
## BFO_0000040 - NA
## GO_0010817 - regulation of hormone levels
## BFO_0000015 - NA
## UBERON_0001062 - anatomical entity
## UBERON_0000000 - processual entity
## BFO_0000020 - NA
## ... 400 more
The usual plotting approach works.
We’ll obtain and ad hoc selection of 15 UBERON term names and visualize the hierarchy.
## resource BFC989 already in cache from http://purl.obolibrary.org/obo/hp.owl
hpents = setup_entities2(hpont_path)
kp = grep("UBER", names(hpents$name), value=TRUE)[21:30]
onto_plot2(hpents, kp)
The prefixes of class names in the ontology give a sense of its scope.
##
## [,1]
## BFO 11
## CHEBI 1849
## CL 1151
## GO 2563
## HP 19434
## HsapDv 12
## MPATH 75
## NBO 64
## PATO 567
## PR 206
## RO 1
## UBERON 5605
To characterize human phenotypes ontologically, CL, GO, CHEBI, and UBERON play significant roles.