lamindb.Reference

class lamindb.Reference(name: str, type: Reference | None = None, is_type: bool = False, abbr: str | None = None, url: str | None = None, pubmed_id: int | None = None, doi: str | None = None, description: str | None = None, text: str | None = None, date: DateType | None = None)

Bases: SQLRecord, CanCurate, TracksRun, TracksUpdates, ValidateFields

References such as internal studies, papers, documents, or URLs.

Example

>>> reference = Reference(
...     name="A Paper Title",
...     abbr="APT",
...     url="https://doi.org/10.1000/xyz123",
...     pubmed_id=12345678,
...     doi="10.1000/xyz123",
...     description="Good paper.",
...     text="Some text I want to be searchable.",
...     date=date(2023, 11, 21),
... ).save()

Simple fields

uid: str

Universal id, valid across DB instances.

name: str

Title or name of the reference document.

description: str | None

A description.

is_type: bool

Distinguish types from instances of the type.

abbr: str | None

An abbreviation for the reference.

url: str | None

URL linking to the reference.

pubmed_id: int | None

A PudMmed ID.

doi: str | None

Digital Object Identifier (DOI) for the reference.

text: str | None

Abstract or full text of the reference to make it searchable.

date: DateType | None

Date of creation or publication of the reference.

is_locked: bool

Whether the record is locked for edits.

created_at: datetime

Time of creation of record.

updated_at: datetime

Time of last update to record.

Relational fields

branch: Branch

Whether record is on a branch or in another “special state”.

space: Space

The space in which the record lives.

created_by: User

Creator of record.

run: Run | None

Run that created record.

type: Reference | None

Type of reference (e.g., ‘Study’, ‘Paper’, ‘Preprint’).

Allows to group reference by type, e.g., internal studies vs. all papers etc.

artifacts: Artifact

Annotated artifacts.

transforms: Artifact

Annotated transforms.

collections: Artifact

Annotated collections.

linked_in_records: Record

Linked in records.

records: Record

Annotated records.

references: Reference

References of this type (can only be non-empty if is_type is True).

projects: Project

Projects that annotate this reference.

Class methods

classmethod filter(*queries, **expressions)

Query records.

Parameters:

  • queries – One or multiple Q objects.

  • expressions – Fields and values passed as Django query expressions.

Return type:

QuerySet

Returns:

A QuerySet.

See also

Examples

>>> ln.ULabel(name="my label").save()
>>> ln.ULabel.filter(name__startswith="my").to_dataframe()
classmethod get(idlike=None, **expressions)

Get a single record.

Parameters:

  • idlike (int | str | None, default: None) – Either a uid stub, uid or an integer id.

  • expressions – Fields and values passed as Django query expressions.

Raises:

docs:lamindb.errors.DoesNotExist – In case no matching record is found.

Return type:

SQLRecord

See also

Examples

ulabel = ln.ULabel.get("FvtpPJLJ")
ulabel = ln.ULabel.get(name="my-label")
classmethod to_dataframe(include=None, features=False, limit=100)

Convert to pd.DataFrame.

By default, shows all direct fields, except updated_at.

Use arguments include or feature to include other data.

Parameters:

  • include (str | list[str] | None, default: None) – Related fields to include as columns. Takes strings of form "ulabels__name", "cell_types__name", etc. or a list of such strings.

  • features (bool | list[str], default: False) – If a list of feature names, filters Feature down to these features. If True, prints all features with dtypes in the core schema module. If "queryset", infers the features used within the set of artifacts or records. Only available for Artifact and Record.

  • limit (int, default: 100) – Maximum number of rows to display from a Pandas DataFrame. Defaults to 100 to reduce database load.

Return type:

DataFrame

Examples

Include the name of the creator in the DataFrame:

>>> ln.ULabel.to_dataframe(include="created_by__name"])

Include display of features for Artifact:

>>> df = ln.Artifact.to_dataframe(features=True)
>>> ln.view(df)  # visualize with type annotations

Only include select features:

>>> df = ln.Artifact.to_dataframe(features=["cell_type_by_expert", "cell_type_by_model"])
classmethod search(string, *, field=None, limit=20, case_sensitive=False)

Search.

Parameters:

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

  • field (str | DeferredAttribute | None, default: None) – The field or fields to search. Search all string fields by default.

  • limit (int | None, default: 20) – Maximum amount of top results to return.

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

Return type:

QuerySet

Returns:

A sorted DataFrame of search results with a score in column score. If return_queryset is True. QuerySet.

See also

filter() lookup()

Examples

>>> ulabels = ln.ULabel.from_values(["ULabel1", "ULabel2", "ULabel3"], field="name")
>>> ln.save(ulabels)
>>> ln.ULabel.search("ULabel2")
classmethod lookup(field=None, return_field=None)

Return an auto-complete object for a field.

Parameters:

  • field (str | DeferredAttribute | None, default: None) – The field to look up the values for. Defaults to first string field.

  • return_field (str | DeferredAttribute | None, default: None) – The field to return. If None, returns the whole record.

  • keep – When multiple records are found for a lookup, how to return the records. - "first": return the first record. - "last": return the last record. - False: return all records.

Return type:

NamedTuple

Returns:

A NamedTuple of lookup information of the field values with a dictionary converter.

See also

search()

Examples

>>> import bionty as bt
>>> bt.settings.organism = "human"
>>> bt.Gene.from_source(symbol="ADGB-DT").save()
>>> lookup = bt.Gene.lookup()
>>> lookup.adgb_dt
>>> lookup_dict = lookup.dict()
>>> lookup_dict['ADGB-DT']
>>> lookup_by_ensembl_id = bt.Gene.lookup(field="ensembl_gene_id")
>>> genes.ensg00000002745
>>> lookup_return_symbols = bt.Gene.lookup(field="ensembl_gene_id", return_field="symbol")
classmethod using(instance)

Use a non-default LaminDB instance.

Parameters:

instance (str | None) – An instance identifier of form “account_handle/instance_name”.

Return type:

QuerySet

Examples

>>> ln.ULabel.using("account_handle/instance_name").search("ULabel7", field="name")
            uid    score
name
ULabel7  g7Hk9b2v  100.0
ULabel5  t4Jm6s0q   75.0
ULabel6  r2Xw8p1z   75.0
classmethod inspect(values, field=None, *, mute=False, organism=None, source=None, from_source=True, strict_source=False)

Inspect if values are mappable to a field.

Being mappable means that an exact match exists.

Parameters:

  • values (list[str] | Series | array) – Values that will be checked against the field.

  • field (str | DeferredAttribute | None, default: None) – The field of values. Examples are 'ontology_id' to map against the source ID or 'name' to map against the ontologies field names.

  • mute (bool, default: False) – Whether to mute logging.

  • organism (str | SQLRecord | None, default: None) – An Organism name or record.

  • source (SQLRecord | None, default: None) – A bionty.Source record that specifies the version to inspect against.

  • strict_source (bool, default: False) – Determines the validation behavior against records in the registry. - If False, validation will include all records in the registry, ignoring the specified source. - If True, validation will only include records in the registry that are linked to the specified source. Note: this parameter won’t affect validation against public sources.

Return type:

bionty.base.dev.InspectResult

See also

validate()

Example:

import bionty as bt

# save some gene records
bt.Gene.from_values(["A1CF", "A1BG", "BRCA2"], field="symbol", organism="human").save()

# inspect gene symbols
gene_symbols = ["A1CF", "A1BG", "FANCD1", "FANCD20"]
result = bt.Gene.inspect(gene_symbols, field=bt.Gene.symbol, organism="human")
assert result.validated == ["A1CF", "A1BG"]
assert result.non_validated == ["FANCD1", "FANCD20"]
classmethod validate(values, field=None, *, mute=False, organism=None, source=None, strict_source=False)

Validate values against existing values of a string field.

Note this is strict_source validation, only asserts exact matches.

Parameters:

  • values (list[str] | Series | array) – Values that will be validated against the field.

  • field (str | DeferredAttribute | None, default: None) – The field of values. Examples are 'ontology_id' to map against the source ID or 'name' to map against the ontologies field names.

  • mute (bool, default: False) – Whether to mute logging.

  • organism (str | SQLRecord | None, default: None) – An Organism name or record.

  • source (SQLRecord | None, default: None) – A bionty.Source record that specifies the version to validate against.

  • strict_source (bool, default: False) – Determines the validation behavior against records in the registry. - If False, validation will include all records in the registry, ignoring the specified source. - If True, validation will only include records in the registry that are linked to the specified source. Note: this parameter won’t affect validation against public sources.

Return type:

ndarray

Returns:

A vector of booleans indicating if an element is validated.

See also

inspect()

Example:

import bionty as bt

bt.Gene.from_values(["A1CF", "A1BG", "BRCA2"], field="symbol", organism="human").save()

gene_symbols = ["A1CF", "A1BG", "FANCD1", "FANCD20"]
bt.Gene.validate(gene_symbols, field=bt.Gene.symbol, organism="human")
#> array([ True,  True, False, False])
classmethod from_values(values, field=None, create=False, organism=None, source=None, mute=False)

Bulk create validated records by parsing values for an identifier such as a name or an id).

Parameters:

  • values (list[str] | Series | array) – A list of values for an identifier, e.g. ["name1", "name2"].

  • field (str | DeferredAttribute | None, default: None) – A SQLRecord field to look up, e.g., bt.CellMarker.name.

  • create (bool, default: False) – Whether to create records if they don’t exist.

  • organism (SQLRecord | str | None, default: None) – A bionty.Organism name or record.

  • source (SQLRecord | None, default: None) – A bionty.Source record to validate against to create records for.

  • mute (bool, default: False) – Whether to mute logging.

Return type:

SQLRecordList

Returns:

A list of validated records. For bionty registries. Also returns knowledge-coupled records.

Notes

For more info, see tutorial: Manage biological ontologies.

Example:

import bionty as bt

# Bulk create from non-validated values will log warnings & returns empty list
ulabels = ln.ULabel.from_values(["benchmark", "prediction", "test"])
assert len(ulabels) == 0

# Bulk create records from validated values returns the corresponding existing records
ulabels = ln.ULabel.from_values(["benchmark", "prediction", "test"], create=True).save()
assert len(ulabels) == 3

# Bulk create records from public reference
bt.CellType.from_values(["T cell", "B cell"]).save()
classmethod standardize(values, field=None, *, return_field=None, return_mapper=False, case_sensitive=False, mute=False, source_aware=True, keep='first', synonyms_field='synonyms', organism=None, source=None, strict_source=False)

Maps input synonyms to standardized names.

Parameters:

  • values (Iterable) – Identifiers that will be standardized.

  • field (str | DeferredAttribute | None, default: None) – The field representing the standardized names.

  • return_field (str | DeferredAttribute | None, default: None) – The field to return. Defaults to field.

  • return_mapper (bool, default: False) – If True, returns {input_value: standardized_name}.

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

  • mute (bool, default: False) – Whether to mute logging.

  • source_aware (bool, default: True) – Whether to standardize from public source. Defaults to True for BioRecord registries.

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

    When keep is False, the returned list of standardized names will contain nested lists in case of duplicates.

    When a field is converted into return_field, keep marks which matches to keep when multiple return_field values map to the same field value.

  • synonyms_field (str, default: 'synonyms') – A field containing the concatenated synonyms.

  • organism (str | SQLRecord | None, default: None) – An Organism name or record.

  • source (SQLRecord | None, default: None) – A bionty.Source record that specifies the version to validate against.

  • strict_source (bool, default: False) – Determines the validation behavior against records in the registry. - If False, validation will include all records in the registry, ignoring the specified source. - If True, validation will only include records in the registry that are linked to the specified source. Note: this parameter won’t affect validation against public sources.

Return type:

list[str] | dict[str, 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.

See also

add_synonym()

Add synonyms.

remove_synonym()

Remove synonyms.

Example:

import bionty as bt

# save some gene records
bt.Gene.from_values(["A1CF", "A1BG", "BRCA2"], field="symbol", organism="human").save()

# standardize gene synonyms
gene_synonyms = ["A1CF", "A1BG", "FANCD1", "FANCD20"]
bt.Gene.standardize(gene_synonyms)
#> ['A1CF', 'A1BG', 'BRCA2', 'FANCD20']

Methods

restore()

Restore from trash onto the main branch.

Return type:

None

delete(permanent=None, **kwargs)

Delete record.

Parameters:

permanent (bool | None, default: None) – Whether to permanently delete the record (skips trash). If None, performs soft delete if the record is not already in the trash.

Return type:

None

Examples

For any SQLRecord object record, call:

>>> record.delete()
save(*args, **kwargs)

Save.

Always saves to the default database.

Return type:

TypeVar(T, bound= SQLRecord)

add_synonym(synonym, force=False, save=None)

Add synonyms to a record.

Parameters:

  • synonym (str | list[str] | Series | array) – The synonyms to add to the record.

  • force (bool, default: False) – Whether to add synonyms even if they are already synonyms of other records.

  • save (bool | None, default: None) – Whether to save the record to the database.

See also

remove_synonym()

Remove synonyms.

Example:

import bionty as bt

# save "T cell" record
record = bt.CellType.from_source(name="T cell").save()
record.synonyms
#> "T-cell|T lymphocyte|T-lymphocyte"

# add a synonym
record.add_synonym("T cells")
record.synonyms
#> "T cells|T-cell|T-lymphocyte|T lymphocyte"
remove_synonym(synonym)

Remove synonyms from a record.

Parameters:

synonym (str | list[str] | Series | array) – The synonym values to remove.

See also

add_synonym()

Add synonyms

Example:

import bionty as bt

# save "T cell" record
record = bt.CellType.from_source(name="T cell").save()
record.synonyms
#> "T-cell|T lymphocyte|T-lymphocyte"

# remove a synonym
record.remove_synonym("T-cell")
record.synonyms
#> "T lymphocyte|T-lymphocyte"
set_abbr(value)

Set value for abbr field and add to synonyms.

Parameters:

value (str) – A value for an abbreviation.

See also

add_synonym()

Example:

import bionty as bt

# save an experimental factor record
scrna = bt.ExperimentalFactor.from_source(name="single-cell RNA sequencing").save()
assert scrna.abbr is None
assert scrna.synonyms == "single-cell RNA-seq|single-cell transcriptome sequencing|scRNA-seq|single cell RNA sequencing"

# set abbreviation
scrna.set_abbr("scRNA")
assert scrna.abbr == "scRNA"
# synonyms are updated
assert scrna.synonyms == "scRNA|single-cell RNA-seq|single cell RNA sequencing|single-cell transcriptome sequencing|scRNA-seq"