Enforce pre-defined validation constraints¶

In a previous guide, you defined validation constraints ad-hoc when initializing Curator objects.

Often, you want to enforce a pre-defined set of validation constraints, like, e.g., the CELLxGENE curator (Curate AnnData based on the CELLxGENE schema).

This guide shows how to subclass Curator to enforce pre-defined constraints.

Define a custom curator¶

Consider the example of electronic health records (EHR). We want to ensure that

every record has the fields disease, phenotype, developmental_stage, and age
values for these fields map against specific versions of pre-defined ontologies

The following implementation achieves the goal by subclassing DataFrameCurator.

EHR Curator¶

import bionty as bt
import pandas as pd
from lamindb.core import DataFrameCurator, Record, logger
from lamindb.core.types import UPathStr, FieldAttr

__version__ = "0.1.0"


class EHRCurator(DataFrameCurator):
    """Custom curation flow for electronic health record data."""

    def __init__(self, data: pd.DataFrame | UPathStr):
        # Curate these columns against the specified fields
        DEFAULT_CATEGORICALS = {
            "disease": bt.Disease.name,
            "phenotype": bt.Phenotype.name,
            "developmental_stage": bt.DevelopmentalStage.name,
        }

        # If columns or values are missing, we substitute with these defaults
        DEFAULT_VALUES = {
            "disease": "normal",
            "development_stage": "unknown",
            "phenotype": "unknown",
        }

        # Validate values onto the following ontology versions
        DEFAULT_SOURCES = {
            "disease": bt.Source.get(
                entity="bionty.Disease", name="mondo", version="2023-04-04"
            ),
            "developmental_stage": bt.Source.get(
                entity="bionty.DevelopmentalStage", name="hsapdv", version="2020-03-10"
            ),
            "phenotype": bt.Source.get(
                entity="bionty.Phenotype",
                name="hp",
                version="2023-06-17",
                organism="human",
            ),
        }

        self.data = data

        for col, default in DEFAULT_VALUES.items():
            if col not in self.data.columns:
                self.data[col] = default
            else:
                self.data[col].fillna(default, inplace=True)

        super().__init__(
            df=self.data,
            categoricals=DEFAULT_CATEGORICALS,
            sources=DEFAULT_SOURCES,
            organism="human",
        )

    def validate(self, organism: str | None = None) -> bool:
        """Validates the internal EHR standard."""
        missing_columns = {"disease", "phenotype", "developmental_stage", "age"} - set(
            self.data.columns
        )
        if missing_columns:
            logger.error(
                f"Columns {', '.join(map(repr, missing_columns))} are missing but required."
            )
            return False

        return DataFrameCurator.validate(self, organism)

Use the custom curator¶

!lamin init --storage ./subclass-curator --schema bionty

→ connected lamindb: testuser1/subclass-curator

import lamindb as ln
import bionty as bt
import pandas as pd
from ehrcurator import EHRCurator

ln.track("2XEr2IA4n1w40000")

→ connected lamindb: testuser1/subclass-curator

/opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/anndata/_io/__init__.py:12: FutureWarning: Importing read_zarr from `anndata._io` is deprecated. Please use anndata.io instead.
  warnings.warn(

→ notebook imports: bionty==0.52.0 ehrcurator lamindb==0.76.15 pandas==2.2.3

→ created Transform('2XEr2IA4'), started new Run('d9kUiVPm') at 2024-11-11 10:29:43 UTC

# create example DataFrame that has all mandatory columns but one ('patient_age') is wrongly named
data = {
    'disease': ['Alzheimer disease', 'Diabetes mellitus', 'Breast cancer', 'Hypertension', 'Asthma'],
    'phenotype': ['Cognitive decline', 'Hyperglycemia', 'Tumor growth', 'Increased blood pressure', 'Airway inflammation'],
    'developmental_stage': ['Adult', 'Adult', 'Adult', 'Adult', 'Child'],
    'patient_age': [70, 55, 60, 65, 12],
}
df = pd.DataFrame(data)
df

Show code cell output Hide code cell output

	disease	phenotype	developmental_stage	patient_age
0	Alzheimer disease	Cognitive decline	Adult	70
1	Diabetes mellitus	Hyperglycemia	Adult	55
2	Breast cancer	Tumor growth	Adult	60
3	Hypertension	Increased blood pressure	Adult	65
4	Asthma	Airway inflammation	Child	12

ehrcurator = EHRCurator(df)
ehrcurator.validate()

# Fix the name of wrongly spelled column
df.columns = df.columns.str.replace("patient_age", "age")
ehrcurator.validate()

# Use lookup objects to curate the values
disease_lo = bt.Disease.public().lookup()
phenotype_lo = bt.Phenotype.public().lookup()
developmental_stage_lo = bt.DevelopmentalStage.public().lookup()

df["disease"] = df["disease"].replace({"Hypertension": disease_lo.hypertensive_disorder.name})
df["phenotype"] = df["phenotype"].replace({
    "Tumor growth": phenotype_lo.neoplasm.name,
    "Airway inflammation": phenotype_lo.bronchitis.name}
)
df["developmental_stage"] = df["developmental_stage"].replace({
    "Adult": developmental_stage_lo.adolescent_stage.name,
    "Child": developmental_stage_lo.child_stage.name
})

ehrcurator.validate()