###### Phenotype [image: .md][image]

Bionty provides access to the following public "Phenotype" ontologies:

1. Human Phenotype

2. Phecodes

3. PATO

4. Mammalian Phenotype

Here we show how to access and search Phenotype ontologies to
standardize new data.

Let us create a public ontology accessor with the ".public()" method,
which chooses a default public ontology source from "Source". It's a
PublicOntology object, which you can think about as a public registry:

 import bionty as bt
 import pandas as pd

 phenotypes = bt.Phenotype.public(organism="human")
 phenotypes

As for registries, you can export the ontology as a "DataFrame":

 df = phenotypes.to_dataframe()
 df.head()

Unlike registries, you can also export it as a Pronto object via
"public.ontology".

##### Look up terms

As for registries, terms can be looked up with auto-complete:

 lookup = phenotypes.lookup()

The "." accessor provides normalized terms (lower case, only contains
alphanumeric characters and underscores):

 lookup.eeg_with_persistent_abnormal_rhythmic_activity

To look up the exact original strings, convert the lookup object to
dict and use the "[]" accessor:

 lookup_dict = lookup.dict()
 lookup_dict["EEG with persistent abnormal rhythmic activity"]

By default, the "name" field is used to generate lookup keys. You can
specify another field to look up:

 lookup = phenotypes.lookup(phenotypes.ontology_id)

 lookup.hp_0000003

##### Search terms

Search behaves in the same way as it does for registries:

 phenotypes.search("dysplasia").head(3)

By default, search also covers synonyms and all other fields
containing strings:

 phenotypes.search("Congenital hip dysplasia").head(3)

Search specific field (by default, search is done on all fields
containing strings):

 phenotypes.search(
 "lack of development of speech and language",
 field=phenotypes.definition,
 ).head()

##### Standardize Phenotype identifiers

Let us generate a "DataFrame" that stores a number of Phenotype
identifiers, some of which corrupted:

 df_orig = pd.DataFrame(
 index=[
 "Specific learning disability",
 "Dystonia",
 "Cerebral hemorrhage",
 "Slurred speech",
 "This phenotype does not exist",
 ]
 )
 df_orig

We can check whether any of our values are validated against the
ontology reference:

 validated = phenotypes.validate(df_orig.index, phenotypes.name)
 df_orig.index[~validated]

##### Ontology source versions

For any given entity, we can choose from a number of versions:

 bt.Source.filter(entity="bionty.Phenotype").to_dataframe()

 # only lists the sources that are currently used
 bt.Source.filter(entity="bionty.Phenotype", currently_used=True).to_dataframe()

When instantiating a Bionty object, we can choose a source or version:

 source = bt.Source.filter(
 name="hp", organism="human"
 ).first()
 phenotypes= bt.Phenotype.public(source=source)
 phenotypes

The currently used ontologies can be displayed using:

 bt.Source.filter(currently_used=True).to_dataframe()