Curate DataFrames and AnnDatas¶

Curating a dataset with LaminDB means three things:

Validate that the dataset matches a desired schema
In case the dataset doesn’t validate, standardize it, e.g., by fixing typos or mapping synonyms
Annotate the dataset by linking it against metadata entities so that it becomes queryable

Curate a DataFrame¶

# pip install 'lamindb[bionty]'
!lamin init --storage ./test-curate --modules bionty

Let’s start with a DataFrame that we’d like to validate.

import lamindb as ln
import bionty as bt
import pandas as pd


df = pd.DataFrame(
    {
        "perturbation": pd.Categorical(["DMSO", "IFNG", "DMSO"]),
        "temperature": [37.2, 36.3, 38.2],
        "cell_type": pd.Categorical(
            [
                "cerebral pyramidal neuron",
                "astrocytic glia",
                "oligodendrocyte",
            ]
        ),
        "assay_ontology_id": pd.Categorical(
            ["EFO:0008913", "EFO:0008913", "EFO:0008913"]
        ),
        "donor": ["D0001", "D0002", None],
    },
    index=["obs1", "obs2", "obs3"],
)
df

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→ connected lamindb: testuser1/test-curate

	perturbation	temperature	cell_type	assay_ontology_id	donor
obs1	DMSO	37.2	cerebral pyramidal neuron	EFO:0008913	D0001
obs2	IFNG	36.3	astrocytic glia	EFO:0008913	D0002
obs3	DMSO	38.2	oligodendrocyte	EFO:0008913	None

Define a schema to validate this dataset.

schema = ln.Schema(
    name="My example schema",
    features=[
        ln.Feature(name="perturbation", dtype=ln.ULabel).save(),
        ln.Feature(name="temperature", dtype=float).save(),
        ln.Feature(name="cell_type", dtype=bt.CellType).save(),
        ln.Feature(
            name="assay_ontology_id", dtype=bt.ExperimentalFactor.ontology_id
        ).save(),
        ln.Feature(name="donor", dtype=str, nullable=True).save(),
    ],
).save()
# display the associated features as a dataframe
schema.features.df()

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	uid	name	dtype	is_type	unit	description	array_rank	array_size	array_shape	proxy_dtype	synonyms	_expect_many	_curation	space_id	type_id	run_id	created_at	created_by_id	_aux	_branch_code
id
1	TbmOCTGMLFJL	perturbation	cat[ULabel]	None	None	None	0	0	None	None	None	True	None	1	None	None	2025-02-27 13:54:48.107000+00:00	1	{'af': {'0': None, '1': True}}	1
2	OUEC5i8v9HC6	temperature	float	None	None	None	0	0	None	None	None	True	None	1	None	None	2025-02-27 13:54:48.115000+00:00	1	{'af': {'0': None, '1': True}}	1
3	D144tFa2O4nE	cell_type	cat[bionty.CellType]	None	None	None	0	0	None	None	None	True	None	1	None	None	2025-02-27 13:54:48.528000+00:00	1	{'af': {'0': None, '1': True}}	1
4	u0vEQlt6YSsX	assay_ontology_id	cat[bionty.ExperimentalFactor.ontology_id]	None	None	None	0	0	None	None	None	True	None	1	None	None	2025-02-27 13:54:48.534000+00:00	1	{'af': {'0': None, '1': True}}	1
5	XRPmRFt98s4W	donor	str	None	None	None	0	0	None	None	None	True	None	1	None	None	2025-02-27 13:54:48.540000+00:00	1	{'af': {'0': None, '1': True}}	1

Create a Curator using the dataset & the schema.

curator = ln.curators.DataFrameCurator(df, schema)

The validate() method validates that your dataset adheres to the criteria defined by the schema. It identifies which values are already validated (exist in our registries) and which are potentially problematic (do not yet exist in our registries).

try:
    curator.validate()
except ln.errors.ValidationError as error:
    print(error)

# check the non-validated terms
curator.cat.non_validated

For cell_type, we saw that “cerebral pyramidal neuron”, “astrocytic glia” are not validated.

First, let’s standardize synonym “astrocytic glia” as suggested

curator.cat.standardize("cell_type")

✓ standardized 1 synonym in "cell_type": "astrocytic glia" → "astrocyte"

# now we have only one non-validated cell type left
curator.cat.non_validated

For “cerebral pyramidal neuron”, let’s understand which cell type in the public ontology might be the actual match.

# to check the correct spelling of categories, pass `public=True` to get a lookup object from public ontologies
# use `lookup = curator.cat.lookup()` to get a lookup object of existing records in your instance
lookup = curator.cat.lookup(public=True)
lookup

# here is an example for the "cell_type" column
cell_types = lookup["cell_type"]
cell_types.cerebral_cortex_pyramidal_neuron

# fix the cell type
df.cell_type = df.cell_type.cat.rename_categories(
    {"cerebral pyramidal neuron": cell_types.cerebral_cortex_pyramidal_neuron.name}
)

For perturbation, we want to add the new values: “DMSO”, “IFNG”

# this adds perturbations that were _not_ validated
curator.cat.add_new_from("perturbation")

# validate again
curator.validate()

Save a curated artifact.

artifact = curator.save_artifact(key="my_datasets/my_curated_dataset.parquet")

artifact.describe()

Curate an AnnData¶

Here we additionally specify which var_index to validate against.

import anndata as ad

X = pd.DataFrame(
    {
        "ENSG00000081059": [1, 2, 3],
        "ENSG00000276977": [4, 5, 6],
        "ENSG00000198851": [7, 8, 9],
        "ENSG00000010610": [10, 11, 12],
        "ENSG00000153563": [13, 14, 15],
        "ENSGcorrupted": [16, 17, 18],
    },
    index=df.index,  # because we already curated the dataframe above, it will validate
)
adata = ad.AnnData(X=X, obs=df)
adata

# define var schema
var_schema = ln.Schema(
    name="my_var_schema",
    itype=bt.Gene.ensembl_gene_id,  # identifier type
    dtype=int,
).save()

# define composite schema
anndata_schema = ln.Schema(
    name="small_dataset1_anndata_schema",
    otype="AnnData",  # object type
    components={"obs": schema, "var": var_schema},
).save()

curator = ln.curators.AnnDataCurator(adata, anndata_schema)
try:
    curator.validate()
except ln.errors.ValidationError as error:
    print(error)

Subset the AnnData to validated genes only:

adata_validated = adata[:, ~adata.var.index.isin(["ENSGcorrupted"])].copy()

Now let’s validate the subsetted object:

curator = ln.curators.AnnDataCurator(adata_validated, anndata_schema)
curator.validate()

The validated AnnData can be subsequently saved as an Artifact:

artifact = curator.save_artifact(key="my_datasets/my_curated_anndata.h5ad")

The saved artifact has been annotated with validated features and labels:

artifact.describe()

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Artifact .h5ad/AnnData
├── General
│   ├── .uid = 'P5C6wPIeTP25hRBd0000'
│   ├── .key = 'my_datasets/my_curated_anndata.h5ad'
│   ├── .size = 25672
│   ├── .hash = 'RdKHw6itdYPBD88Pjoxe4g'
│   ├── .n_observations = 3
│   ├── .path = /home/runner/work/lamindb/lamindb/docs/test-curate/.lamindb/P5C6wPIeTP25hRBd0000.h5ad
│   ├── .created_by = testuser1 (Test User1)
│   └── .created_at = 2025-02-27 13:54:55
├── Dataset features/schema
│   ├── var • 5                     [bionty.Gene]                                                       
│   │   TCF7                        int                                                                 
│   │   PDCD1                       int                                                                 
│   │   CD8A                        int                                                                 
│   │   CD4                         int                                                                 
│   │   CD3E                        int                                                                 
│   └── obs • 5                     [Feature]                                                           
│       assay_ontology_id           cat[bionty.ExperimentalF…  single-cell RNA sequencing               
│       cell_type                   cat[bionty.CellType]       astrocyte, cerebral cortex pyramidal neu…
│       perturbation                cat[ULabel]                DMSO, IFNG                               
│       temperature                 float                                                               
│       donor                       str                                                                 
└── Labels
    └── .cell_types                 bionty.CellType            astrocyte, oligodendrocyte, cerebral cor…
        .experimental_factors       bionty.ExperimentalFactor  single-cell RNA sequencing               
        .ulabels                    ULabel                     IFNG, DMSO

Standardize an AnnData¶

If you need more control, you can access the underlying "var" and "obs" DataFrameCurator objects directly.

curator.slots["var"]
curator.slots["obs"]

# revert the previous cell type standardization
df["cell_type"] = df["cell_type"].cat.rename_categories(
    {"astrocyte": "astrocytic glia"}
)
# an AnnData where a cell type matches a synonym
adata_with_synonym = ad.AnnData(X=adata_validated.X, var=adata_validated.var, obs=df)
adata_with_synonym

AnnData object with n_obs × n_vars = 3 × 5
    obs: 'perturbation', 'temperature', 'cell_type', 'assay_ontology_id', 'donor'

curator = ln.curators.AnnDataCurator(adata_with_synonym, anndata_schema)
try:
    curator.validate()
except ln.errors.ValidationError as error:
    print(error)

✓ "perturbation" is validated against ULabel.name

• mapping "cell_type" on CellType.name

!   1 term is not validated: 'astrocytic glia'
    1 synonym found: "astrocytic glia" → "astrocyte"
    → curate synonyms via .standardize("cell_type")

✓ "assay_ontology_id" is validated against ExperimentalFactor.ontology_id

1 term is not validated: 'astrocytic glia'
    1 synonym found: "astrocytic glia" → "astrocyte"
    → curate synonyms via .standardize("cell_type")

curator.slots["obs"].cat.standardize("cell_type")

✓ standardized 1 synonym in "cell_type": "astrocytic glia" → "astrocyte"

curator.validate()

✓ "perturbation" is validated against ULabel.name

✓ "cell_type" is validated against CellType.name

✓ "assay_ontology_id" is validated against ExperimentalFactor.ontology_id

Summary¶

We’ve walked through the process of validating, standardizing, and annotating datasets going through these key steps:

Defining validation criteria
Validating data against existing registries
Adding new validated entries to registries
Annotating artifacts with validated metadata

By following these steps, you can ensure your data is standardized and well-curated.

If you have datasets that aren’t DataFrame-like or AnnData-like, read: Curate datasets of any format.