In vjcitn/ontoProc: processing of ontologies of anatomy, cell lines, and so on

Introduction

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_entities will use owlready2 python modules to parse the OWL and produce an instance of S3 class owlents.

library(ontoProc)
clont_path = owl2cache(url="http://purl.obolibrary.org/obo/cl.owl")
cle = setup_entities(clont_path)
cle

A plot method is available. Given a vector of tags as reported in OWL (no colons are used), the plot method produces an ontologyIndex instance and runs onto_plot2 on the result.

sel = c("CL_0000492", "CL_0001054", "CL_0000236", 
"CL_0000625", "CL_0000576", 
"CL_0000623", "CL_0000451", "CL_0000556")
plot(cle, sel)

Illustration with Human Phenotype ontology

We'll obtain and ad hoc selection of 15 UBERON term names and visualize the hierarchy.

hpont_path = owl2cache(url="http://purl.obolibrary.org/obo/hp.owl")
hpents = setup_entities(hpont_path)
kp = grep("UBER", hpents$clnames, value=TRUE)[21:35]
plot(hpents, kp)

The prefixes of class names in the ontology give a sense of its scope.

t(t(table(sapply(strsplit(hpents$clnames, "_"), "[", 1))))

To characterize human phenotypes ontologically, CL, GO, CHEBI, and UBERON play significant roles.

vjcitn/ontoProc documentation built on March 23, 2024, 7:23 a.m.