Introduction to ontologyX
Daniel Greene
2016-10-27
ontologyIndex is the foundation of the ‘ontologyX’ packages:
ontologyIndex, for representing ontologies as R objects and enabling simple queries,ontologySimilarity, for computing semantic similarity between ontological terms and annotations,ontologyPlot for visualising sets of ontological terms with various graphical options.
The functionality of the ontologyIndex package is centered around ontology_index objects: simple R representations of ontologies consisting of lists and vectors of term properties (ID, label, etc.) which are named by term so that simple look-ups by term can be performed. Ontologies encoded in OBO or OWL format can be read into R using the function get_ontology, or created explicitly using the function ontology_index. The minimum requirement for creating an ontology_index object is that each term should have an ID, a label and a set of parents/superclasses (possibly empty). The package comes with three such ready-made ontology_index objects: hpo, mpo and go, encapsulating the Human Phenotype Ontology (HPO), Mammalian Phenotype Ontology (MPO) and Gene Ontology (GO) respectively, each loadable with data. Here we’ll demonstrate the package using the HPO.
library(ontologyIndex)
data(hpo)
Note: to use an up-to-date version of a given ontology, download the relevant .obo or .owl file and read it into R using the function get_ontology:
ontology <- get_ontology(file)
The ontology_index object is just a list of ‘vectors and lists’ of term properties, indexed by the IDs of the terms:
## property class
## 1 id character
## 2 name character
## 3 parents list
## 4 children list
## 5 ancestors list
## 6 obsolete logical
The ‘special’ term properties, which all ontology_index objects contain, are id, name, parents, children and ancestors. These are the properties which the functions in the ontologyIndex package operate on - however, additional properties per term - custom properties, or whichever properties are contained in the original file - can also be read in and queried in the same way (see the vignette ‘Creating an ontology_index’). Custom functions which operate on additional properties can also be defined. The children and ancestors properties are based on propagating the parent relationships for each node. If reading an ontology from a file, the definition of the parents of each term can be defined as desired - for instance it could reflect is_a/‘superclass’ relationships, or part_of relationships, or both (see ‘Creating an ontology_index’ and ?get_ontology for more details).
You can use the function get_term_property to query the ontology_index object, and retrieve a particular attribute for a single term. For instance:
get_term_property(ontology=hpo, property="ancestors", term="HP:0001873", as_names=TRUE)
## HP:0000001
## "All"
## HP:0000118
## "Phenotypic abnormality"
## HP:0001871
## "Abnormality of blood and blood-forming tissues"
## HP:0001872
## "Abnormality of thrombocytes"
## HP:0011873
## "Abnormal platelet count"
## HP:0001873
## "Thrombocytopenia"
However you can also look up properties for a given term using [ and [[ as appropriate, since an ontology_index just a list. This is the best way to use the ontology_index if you are operating on multiple terms as it’s faster.
hpo$name["HP:0001873"]
## HP:0001873
## "Thrombocytopenia"
hpo$id[grep(x=hpo$name, pattern="Thrombocytopenia")]
## HP:0001873
## "HP:0001873"
hpo$ancestors[["HP:0001873"]]
## [1] "HP:0000001" "HP:0000118" "HP:0001871" "HP:0001872" "HP:0011873"
## [6] "HP:0001873"
hpo$name[hpo$ancestors[["HP:0001873"]]]
## HP:0000001
## "All"
## HP:0000118
## "Phenotypic abnormality"
## HP:0001871
## "Abnormality of blood and blood-forming tissues"
## HP:0001872
## "Abnormality of thrombocytes"
## HP:0011873
## "Abnormal platelet count"
## HP:0001873
## "Thrombocytopenia"
Removing redundant terms
A set of terms (i.e. a character vector of term IDs) may contain redundant terms. The function minimal_set removes such pairs so as to leave a minimal set of terms, in the sense of the ontology’s directed acyclic graph.
terms <- c("HP:0001871", "HP:0001873", "HP:0011877")
hpo$name[terms]
## HP:0001871
## "Abnormality of blood and blood-forming tissues"
## HP:0001873
## "Thrombocytopenia"
## HP:0011877
## "Increased mean platelet volume"
minimal <- minimal_set(hpo, terms)
hpo$name[minimal]
## HP:0001873 HP:0011877
## "Thrombocytopenia" "Increased mean platelet volume"
Finding all ancestors of a set of terms
To find all the ancestors of a set of terms, i.e. all the terms which are an ancestor of any term in the given set, on can use the get_ancestors function:
get_ancestors(hpo, c("HP:0001873", "HP:0011877"))
## [1] "HP:0000001" "HP:0000118" "HP:0001871" "HP:0001872" "HP:0011873"
## [6] "HP:0001873" "HP:0011876" "HP:0011877"
Operating on subclasses
There are functions which allow set operations with respect to descendancy: intersection_with_descendants, exclude_descendants and prune_descendants. Each function accepts a set of terms terms and a set of root terms roots. We here demonstrate how these functions work on a set of terms containing:
"HP:0000707": Abnormality of the nervous system,"HP:0000924": Abnormality of the skeletal system,"HP:0000006": Autosomal dominant inheritance.
The panels below show the original, untransformed set of terms, and the set of terms after each of the above mentioned functions has been called on it, passing "HP:0000118": ‘Phenotypic abnormality’ as the roots argument (terms present in the set highlighted in green, those not present in grey). ontologyPlot was used to create the diagrams.
For more details see the help file for the individual functions, e.g. ?exclude_descendants. Note that to perform analagous operations with respect to sets of ancestors, one can use the get_ancestors function in conjunction with the base R set functions, e.g. setdiff and intersect.
Additional ontological functionality
The packages ontologySimilarity and ontologyPlot can be used to calculate semantic similarity between and visualise terms and sets of terms respectively: see the corresponding vignettes for more details.