CellLine¶
lamindb provides access to the following public CellLine ontologies through bionty:
Here we show how to access and search CellLine ontologies to standardize new data.
import bionty as bt
import pandas as pd
PublicOntology objects¶
Let us create a public ontology accessor with .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:
celllines = bt.CellLine.public(organism="all")
celllines
→ connected lamindb: testuser1/test-public-ontologies
PublicOntology
Entity: CellLine
Organism: all
Source: clo, 2022-03-21
#terms: 39037
As for registries, you can export the ontology as a DataFrame
:
df = celllines.df()
df.head()
name | definition | synonyms | parents | |
---|---|---|---|---|
ontology_id | ||||
CLO:0000000 | cell line cell culturing | a maintaining cell culture process that keeps ... | None | [] |
CLO:0000001 | cell line cell | A cultured cell that is part of a cell line - ... | None | [] |
CLO:0000002 | suspension cell line culturing | suspension cell line culturing is a cell line ... | None | [CLO:0000000] |
CLO:0000003 | adherent cell line culturing | adherent cell line culturing is a cell line cu... | None | [CLO:0000000] |
CLO:0000004 | cell line cell modification | a material processing that modifies an existin... | None | [] |
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 = celllines.lookup()
The .
accessor provides normalized terms (lower case, only contains alphanumeric characters and underscores):
lookup.hek293
CellLine(ontology_id='CLO:0001230', name='HEK293', definition=None, synonyms='293|HEK-293|293 HEK|293 cell|Human Embryonic Kidney 293|HEK 293', parents=array(['CLO:0037236'], dtype=object))
To look up the exact original strings, convert the lookup object to dict and use the []
accessor:
lookup_dict = lookup.dict()
lookup_dict["HEK293"]
CellLine(ontology_id='CLO:0001230', name='HEK293', definition=None, synonyms='293|HEK-293|293 HEK|293 cell|Human Embryonic Kidney 293|HEK 293', parents=array(['CLO:0037236'], dtype=object))
By default, the name
field is used to generate lookup keys. You can specify another field to look up:
lookup = celllines.lookup(celllines.ontology_id)
lookup.clo_0000469
CellLine(ontology_id='CLO:0000469', name='immortal cat mixed endoderm/mesoderm-derived structure-derived cell line cell', definition='An immortal mixed endoderm/mesoderm-derived structure-derived cell line cell that derives from cat.', synonyms=None, parents=array(['CLO:0000213'], dtype=object))
Search terms¶
Search behaves in the same way as it does for registries:
celllines.search("hek293").head(3)
name | definition | synonyms | parents | |
---|---|---|---|---|
ontology_id | ||||
CLO:0001230 | HEK293 | None | 293|HEK-293|293 HEK|293 cell|Human Embryonic K... | [CLO:0037236] |
CLO:0037237 | 293-derived cell | None | 293|HEK-293|293 HEK|HEK293|HEK 293 | [CLO:0037236] |
CLO:0037352 | ER-alpha-UAS-bla HEK293 | HEK293 cells modified with a beta-lactamase re... | None | [CLO:0037237] |
By default, search also covers synonyms and all other fileds containing strings:
celllines.search("Human Embryonic Kidney 293").head(3)
name | definition | synonyms | parents | |
---|---|---|---|---|
ontology_id | ||||
CLO:0001230 | HEK293 | None | 293|HEK-293|293 HEK|293 cell|Human Embryonic K... | [CLO:0037236] |
CLO:0037372 | HEK293T cell | None | 293T|293-T|HEK 293 T|HEK-293T|HEK293T|293tsA16... | [CLO:0037237] |
CLO:0037373 | HEK293T-derived cell | None | Human Embryonic Kidney 293T-derived cell | [CLO:0037237] |
Search specific field (by default, search is done on all fields containing strings):
celllines.search(
"suspension cell line",
field=celllines.definition,
).head()
name | definition | synonyms | parents | |
---|---|---|---|---|
ontology_id | ||||
CLO:0000002 | suspension cell line culturing | suspension cell line culturing is a cell line ... | None | [CLO:0000000] |
Standardize CellLine identifiers¶
Let us generate a DataFrame
that stores a number of CellLine identifiers, some of which corrupted:
df_orig = pd.DataFrame(
index=[
"253D cell",
"HEK293",
"2C1H7 cell",
"283TAg cell",
"This cellline does not exist",
]
)
df_orig
253D cell |
---|
HEK293 |
2C1H7 cell |
283TAg cell |
This cellline does not exist |
We can check whether any of our values are validated against the ontology reference:
validated = celllines.validate(df_orig.index, celllines.name)
df_orig.index[~validated]
! 1 unique term (20.00%) is not validated: 'This cellline does not exist'
Index(['This cellline does not exist'], dtype='object')
Ontology source versions¶
For any given entity, we can choose from a number of versions:
bt.Source.filter(entity="bionty.CellLine").df()
# only lists the sources that are currently used
bt.Source.filter(entity="bionty.CellLine", currently_used=True).df()
uid | entity | organism | name | in_db | currently_used | description | url | md5 | source_website | space_id | dataframe_artifact_id | version | run_id | created_at | created_by_id | _aux | branch_id | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
id | ||||||||||||||||||
14 | 6LyRtvz8 | bionty.CellLine | all | clo | False | True | Cell Line Ontology | s3://bionty-assets/df_all__clo__2022-03-21__Ce... | None | https://bioportal.bioontology.org/ontologies/CLO | 1 | None | 2022-03-21 | None | 2025-07-14 06:41:44.843000+00:00 | 1 | None | 1 |
When instantiating a Bionty object, we can choose a source or version:
source = bt.Source.filter(
name="clo", organism="all"
).first()
celllines= bt.CellLine.public(source=source)
celllines
PublicOntology
Entity: CellLine
Organism: all
Source: clo, 2022-03-21
#terms: 39037
The currently used ontologies can be displayed using:
bt.Source.filter(currently_used=True).df()