bionty.Bionty#

class bionty.Bionty(source=None, version=None, organism=None, *, include_id_prefixes=None, **kwargs)#

Bases: object

Bionty base model.

Attributes

fields property#

All Bionty entity fields.

organism property#

The name of Organism Bionty.

source property#

Name of the source.

version property#

The name of version entity Bionty.

Methods

df()#

Pandas DataFrame of the ontology.

Return type:

DataFrame

Returns:

A Pandas DataFrame of the ontology.

Examples

>>> import bionty as bt
>>> bt.Gene().df()
diff(compare_to, **kwargs)#

Determines a diff between two Bionty objects’ ontologies.

Parameters:

  • compare_to (Bionty) – Bionty object that must be of the same class as the calling object.

  • kwargs – Are passed to pd.DataFrame.compare()

Return type:

Tuple[DataFrame, DataFrame]

Returns:

A tuple of two DataFrames –

  1. New entries.

  2. A pd.DataFrame.compare result which denotes all changes in self and other.

Examples

>>> import bionty as bt
>>> disease_bt_1 = bt.Disease(source="mondo", version="2023-04-04")
>>> disease_bt_2 = bt.Disease(source="mondo", version="2023-04-04")
>>> new_entries, modified_entries = disease_bt_1.diff(disease_bt_2)
>>> print(new_entries.head())
>>> print(modified_entries.head())
inspect(values, field, *, mute=False, **kwargs)#

Inspect a list of values against a field of entity reference.

Parameters:

  • values (Iterable) – Identifiers that will be checked against the field.

  • field (BiontyField) – The BiontyField of the ontology to compare against. Examples are ‘ontology_id’ to map against the source ID or ‘name’ to map against the ontologies field names.

  • return_df – Whether to return a Pandas DataFrame.

Return type:

InspectResult

Returns:

  • A Dictionary of “validated” and “not_validated” identifiers

  • If return_df: A DataFrame indexed by identifiers with a boolean

    __validated__ column indicating compliance validation.

Examples

>>> import bionty as bt
>>> gene_bt = bt.Gene()
>>> gene_symbols = ["A1CF", "A1BG", "FANCD1", "FANCD20"]
>>> gene_bt.inspect(gene_symbols, field=gene_bt.symbol)
lookup(field=None)#

An auto-complete object for a Bionty field.

Parameters:

field (Union[BiontyField, str, None], default: None) – The field to lookup the values for. Defaults to ‘name’.

Return type:

Tuple

Returns:

A NamedTuple of lookup information of the field values.

Examples

>>> import bionty as bt
>>> lookup = bt.CellType().lookup()
>>> lookup.cd103_positive_dendritic_cell
>>> lookup_dict = lookup.dict()
>>> lookup['CD103-positive dendritic cell']
map_synonyms(values, *, return_mapper=False, case_sensitive=False, keep='first', synonyms_field='synonyms', field=None)#

Maps input synonyms to standardized names.

Return type:

Union[Dict[str, str], List[str]]

search(string, *, field=None, limit=None, case_sensitive=False, synonyms_field='synonyms')#

Search a given string against a Bionty field.

Parameters:

  • string (str) – The input string to match against the field values.

  • field (Union[BiontyField, str, None], default: None) – The BiontyField of the ontology the input string is matching against.

  • top_hit – Default is False, return all entries ranked by matching ratios. If True, only return the top match.

  • case_sensitive (bool, default: False) – Whether the match is case sensitive.

  • synonyms_field (Union[BiontyField, str, None], default: 'synonyms') – By default also search against the synonyms (If None, skips search).

Return type:

DataFrame

Returns:

Ranked search results.

Examples

>>> import bionty as bt
>>> celltype_bt = bt.CellType()
>>> celltype_bt.search("gamma delta T cell")
standardize(values, *, return_mapper=False, case_sensitive=False, mute=False, keep='first', synonyms_field='synonyms', field=None)#

Convert into standardized names.

Parameters:

  • synonyms – Iterable Synonyms that will be standardized.

  • return_mapper (bool, default: False) – bool = False If True, returns {input_synonym1: standardized_name1}.

  • case_sensitive (bool, default: False) – bool = False Whether the mapping is case sensitive.

  • organism – Optional[str] Map only against this organism related entries.

  • keep (Literal['first', 'last', False], default: 'first') –

    Literal[“first”, “last”, False] = “first” When a synonym maps to multiple names, determines which duplicates to mark as pd.DataFrame.duplicated

    • ”first”: returns the first mapped standardized name

    • ”last”: returns the last mapped standardized name

    • False: returns all mapped standardized name

  • synonyms_field (Union[BiontyField, str], default: 'synonyms') – str = “synonyms” A field containing the concatenated synonyms.

  • field (Union[BiontyField, str, None], default: None) – Optional[str] The field representing the standardized names.

Return type:

Union[Dict[str, str], List[str]]

Returns:

If return_mapper is False – a list of standardized names. Otherwise, a dictionary of mapped values with mappable synonyms as keys and standardized names as values.

Examples

>>> import bionty as bt
>>> gene_bt = bt.Gene()
>>> gene_symbols = ["A1CF", "A1BG", "FANCD1", "FANCD20"]
>>> standardized_symbols = gene_bt.standardize(gene_symbols, gene_bt.symbol)
validate(values, field, *, mute=False, **kwargs)#

Validate a list of values against a field of entity reference.

Return type:

ndarray