gh-k-dense-ai-claude-scientific-skills-scientific-skills/skills/lamindb/references/ontologies.md

LaminDB Ontology Management

This document covers biological ontology management in LaminDB through the Bionty plugin, including accessing, searching, and annotating data with standardized biological terms.

Overview

LaminDB integrates the bionty plugin to manage standardized biological ontologies, enabling consistent metadata curation and data annotation across research projects. Bionty provides access to 20+ curated biological ontologies covering genes, proteins, cell types, tissues, diseases, and more.

Available Ontologies

LaminDB provides access to multiple curated ontology sources:

Registry	Ontology Source	Description
Gene	Ensembl	Genes across organisms (human, mouse, etc.)
Protein	UniProt	Protein sequences and annotations
CellType	Cell Ontology (CL)	Standardized cell type classifications
CellLine	Cell Line Ontology (CLO)	Cell line annotations
Tissue	Uberon	Anatomical structures and tissues
Disease	Mondo, DOID	Disease classifications
Phenotype	Human Phenotype Ontology (HPO)	Phenotypic abnormalities
Pathway	Gene Ontology (GO)	Biological pathways and processes
ExperimentalFactor	Experimental Factor Ontology (EFO)	Experimental variables
DevelopmentalStage	Various	Developmental stages across organisms
Ethnicity	HANCESTRO	Human ancestry ontology
Drug	DrugBank	Drug compounds
Organism	NCBItaxon	Taxonomic classifications

Installation and Import

# Install bionty (included with lamindb)
pip install lamindb

# Import
import lamindb as ln
import bionty as bt

Importing Public Ontologies

Populate your registry with a public ontology source:

# Import Cell Ontology
bt.CellType.import_source()

# Import organism-specific genes
bt.Gene.import_source(organism="human")
bt.Gene.import_source(organism="mouse")

# Import tissues
bt.Tissue.import_source()

# Import diseases
bt.Disease.import_source(source="mondo")  # Mondo Disease Ontology
bt.Disease.import_source(source="doid")   # Disease Ontology

Searching and Accessing Records

Keyword Search

# Search cell types
bt.CellType.search("T cell").to_dataframe()
bt.CellType.search("gamma-delta").to_dataframe()

# Search genes
bt.Gene.search("CD8").to_dataframe()
bt.Gene.search("TP53").to_dataframe()

# Search diseases
bt.Disease.search("cancer").to_dataframe()

# Search tissues
bt.Tissue.search("brain").to_dataframe()

Auto-Complete Lookup

For registries with fewer than 100k records:

# Create lookup object
cell_types = bt.CellType.lookup()

# Access by name (auto-complete in IDEs)
t_cell = cell_types.t_cell
hsc = cell_types.hematopoietic_stem_cell

# Similarly for other registries
genes = bt.Gene.lookup()
cd8a = genes.cd8a

Exact Field Matching

# By ontology ID
cell_type = bt.CellType.get(ontology_id="CL:0000798")
disease = bt.Disease.get(ontology_id="MONDO:0004992")

# By name
cell_type = bt.CellType.get(name="T cell")
gene = bt.Gene.get(symbol="CD8A")

# By Ensembl ID
gene = bt.Gene.get(ensembl_gene_id="ENSG00000153563")

Ontological Hierarchies

Exploring Relationships

# Get a cell type
gdt_cell = bt.CellType.get(ontology_id="CL:0000798")  # gamma-delta T cell

# View direct parents
gdt_cell.parents.to_dataframe()

# View all ancestors recursively
ancestors = []
current = gdt_cell
while current.parents.exists():
    parent = current.parents.first()
    ancestors.append(parent)
    current = parent

# View direct children
gdt_cell.children.to_dataframe()

# View all descendants recursively
gdt_cell.query_children().to_dataframe()

Visualizing Hierarchies

# Visualize parent hierarchy
gdt_cell.view_parents()

# Include children in visualization
gdt_cell.view_parents(with_children=True)

# Get all related terms for visualization
t_cell = bt.CellType.get(name="T cell")
t_cell.view_parents(with_children=True)  # Shows T cell subtypes

Standardizing and Validating Data

Validation

Check if terms exist in the ontology:

# Validate cell types
bt.CellType.validate(["T cell", "B cell", "invalid_cell"])
# Returns: [True, True, False]

# Validate genes
bt.Gene.validate(["CD8A", "TP53", "FAKEGENE"], organism="human")
# Returns: [True, True, False]

# Check which terms are invalid
terms = ["T cell", "fat cell", "neuron", "invalid_term"]
invalid = [t for t, valid in zip(terms, bt.CellType.validate(terms)) if not valid]
print(f"Invalid terms: {invalid}")

Standardization with Synonyms

Convert non-standard terms to validated names:

# Standardize cell type names
bt.CellType.standardize(["fat cell", "blood forming stem cell"])
# Returns: ['adipocyte', 'hematopoietic stem cell']

# Standardize genes
bt.Gene.standardize(["BRCA-1", "p53"], organism="human")
# Returns: ['BRCA1', 'TP53']

# Handle mixed valid/invalid terms
terms = ["T cell", "T lymphocyte", "invalid"]
standardized = bt.CellType.standardize(terms)
# Returns standardized names where possible

Loading Validated Records

# Load records from values (including synonyms)
records = bt.CellType.from_values(["fat cell", "blood forming stem cell"])

# Returns list of CellType records
for record in records:
    print(record.name, record.ontology_id)

# Use with gene symbols
genes = bt.Gene.from_values(["CD8A", "CD8B"], organism="human")

Annotating Datasets

Annotating AnnData

import anndata as ad
import lamindb as ln

# Load example data
adata = ad.read_h5ad("data.h5ad")

# Validate and retrieve matching records
cell_types = bt.CellType.from_values(adata.obs.cell_type)

# Create artifact with annotations
artifact = ln.Artifact.from_anndata(
    adata,
    key="scrna/annotated_data.h5ad",
    description="scRNA-seq data with validated cell type annotations"
).save()

# Link ontology records to artifact
artifact.feature_sets.add_ontology(cell_types)

Annotating DataFrames

import pandas as pd

# Create DataFrame with biological entities
df = pd.DataFrame({
    "cell_type": ["T cell", "B cell", "NK cell"],
    "tissue": ["blood", "spleen", "liver"],
    "disease": ["healthy", "lymphoma", "healthy"]
})

# Validate and standardize
df["cell_type"] = bt.CellType.standardize(df["cell_type"])
df["tissue"] = bt.Tissue.standardize(df["tissue"])

# Create artifact
artifact = ln.Artifact.from_dataframe(
    df,
    key="metadata/sample_info.parquet"
).save()

# Link ontology records
cell_type_records = bt.CellType.from_values(df["cell_type"])
tissue_records = bt.Tissue.from_values(df["tissue"])

artifact.feature_sets.add_ontology(cell_type_records)
artifact.feature_sets.add_ontology(tissue_records)

Managing Custom Terms and Hierarchies

Adding Custom Terms

# Register new term not in public ontology
my_celltype = bt.CellType(name="my_novel_T_cell_subtype").save()

# Establish parent-child relationship
parent = bt.CellType.get(name="T cell")
my_celltype.parents.add(parent)

# Verify relationship
my_celltype.parents.to_dataframe()
parent.children.to_dataframe()  # Should include my_celltype

Adding Synonyms

# Add synonyms for standardization
hsc = bt.CellType.get(name="hematopoietic stem cell")
hsc.add_synonym("HSC")
hsc.add_synonym("blood stem cell")
hsc.add_synonym("hematopoietic progenitor")

# Set abbreviation
hsc.set_abbr("HSC")

# Now standardization works with synonyms
bt.CellType.standardize(["HSC", "blood stem cell"])
# Returns: ['hematopoietic stem cell', 'hematopoietic stem cell']

Creating Custom Hierarchies

# Build custom cell type hierarchy
immune_cell = bt.CellType.get(name="immune cell")

# Add custom subtypes
my_subtype1 = bt.CellType(name="custom_immune_subtype_1").save()
my_subtype2 = bt.CellType(name="custom_immune_subtype_2").save()

# Link to parent
my_subtype1.parents.add(immune_cell)
my_subtype2.parents.add(immune_cell)

# Create sub-subtypes
my_subsubtype = bt.CellType(name="custom_sub_subtype").save()
my_subsubtype.parents.add(my_subtype1)

# Visualize custom hierarchy
immune_cell.view_parents(with_children=True)

Multi-Organism Support

For organism-aware registries like Gene:

# Set global organism
bt.settings.organism = "human"

# Validate human genes
bt.Gene.validate(["TCF7", "CD8A"], organism="human")

# Load genes for specific organism
human_genes = bt.Gene.from_values(["CD8A", "TP53"], organism="human")
mouse_genes = bt.Gene.from_values(["Cd8a", "Trp53"], organism="mouse")

# Search organism-specific genes
bt.Gene.search("CD8", organism="human").to_dataframe()
bt.Gene.search("Cd8", organism="mouse").to_dataframe()

# Switch organism context
bt.settings.organism = "mouse"
genes = bt.Gene.from_source(symbol="Ap5b1")

Public Ontology Lookup

Access terms from public ontologies without importing:

# Interactive lookup in public sources
cell_types_public = bt.CellType.lookup(public=True)

# Access public terms
hepatocyte = cell_types_public.hepatocyte

# Import specific term
hepatocyte_local = bt.CellType.from_source(name="hepatocyte")

# Or import by ontology ID
specific_cell = bt.CellType.from_source(ontology_id="CL:0000182")

Version Tracking

LaminDB automatically tracks ontology versions:

# View current source versions
bt.Source.filter(currently_used=True).to_dataframe()

# Check which source a record derives from
cell_type = bt.CellType.get(name="hepatocyte")
cell_type.source  # Returns Source metadata

# View source details
source = cell_type.source
print(source.name)        # e.g., "cl"
print(source.version)     # e.g., "2023-05-18"
print(source.url)         # Ontology URL

Ontology Integration Workflows

Workflow 1: Validate Existing Data

# Load data with biological annotations
adata = ad.read_h5ad("uncurated_data.h5ad")

# Validate cell types
validation = bt.CellType.validate(adata.obs.cell_type)

# Identify invalid terms
invalid_idx = [i for i, v in enumerate(validation) if not v]
invalid_terms = adata.obs.cell_type.iloc[invalid_idx].unique()
print(f"Invalid cell types: {invalid_terms}")

# Fix invalid terms manually or with standardization
adata.obs["cell_type"] = bt.CellType.standardize(adata.obs.cell_type)

# Re-validate
validation = bt.CellType.validate(adata.obs.cell_type)
assert all(validation), "All terms should now be valid"

Workflow 2: Curate and Annotate

import lamindb as ln

ln.track()  # Start tracking

# Load data
df = pd.read_csv("experimental_data.csv")

# Standardize using ontologies
df["cell_type"] = bt.CellType.standardize(df["cell_type"])
df["tissue"] = bt.Tissue.standardize(df["tissue"])

# Create curated artifact
artifact = ln.Artifact.from_dataframe(
    df,
    key="curated/experiment_2025_10.parquet",
    description="Curated experimental data with ontology-validated annotations"
).save()

# Link ontology records
artifact.feature_sets.add_ontology(bt.CellType.from_values(df["cell_type"]))
artifact.feature_sets.add_ontology(bt.Tissue.from_values(df["tissue"]))

ln.finish()  # Complete tracking

Workflow 3: Cross-Organism Gene Mapping

# Get human genes
human_genes = ["CD8A", "CD8B", "TP53"]
human_records = bt.Gene.from_values(human_genes, organism="human")

# Find mouse orthologs (requires external mapping)
# LaminDB doesn't provide built-in ortholog mapping
# Use external tools like Ensembl BioMart or homologene

mouse_orthologs = ["Cd8a", "Cd8b", "Trp53"]
mouse_records = bt.Gene.from_values(mouse_orthologs, organism="mouse")

Querying Ontology-Annotated Data

# Find all datasets with specific cell type
t_cell = bt.CellType.get(name="T cell")
ln.Artifact.filter(feature_sets__cell_types=t_cell).to_dataframe()

# Find datasets measuring specific genes
cd8a = bt.Gene.get(symbol="CD8A", organism="human")
ln.Artifact.filter(feature_sets__genes=cd8a).to_dataframe()

# Query across ontology hierarchy
# Find all datasets with T cell or T cell subtypes
t_cell_subtypes = t_cell.query_children()
ln.Artifact.filter(
    feature_sets__cell_types__in=t_cell_subtypes
).to_dataframe()

Best Practices

1. Import ontologies first: Call import_source() before validation
2. Use standardization: Leverage synonym mapping to handle variations
3. Validate early: Check terms before creating artifacts
4. Set organism context: Specify organism for gene-related queries
5. Add custom synonyms: Register common variations in your domain
6. Use public lookup: Access lookup(public=True) for term discovery
7. Track versions: Monitor ontology source versions for reproducibility
8. Build hierarchies: Link custom terms to existing ontology structures
9. Query hierarchically: Use query_children() for comprehensive searches
10. Document mappings: Track custom term additions and relationships

Common Ontology Operations

# Check if term exists
exists = bt.CellType.filter(name="T cell").exists()

# Count terms in registry
n_cell_types = bt.CellType.filter().count()

# Get all terms with specific parent
immune_cells = bt.CellType.filter(parents__name="immune cell")

# Find orphan terms (no parents)
orphans = bt.CellType.filter(parents__isnull=True)

# Get recently added terms
from datetime import datetime, timedelta
recent = bt.CellType.filter(
    created_at__gte=datetime.now() - timedelta(days=7)
)