lamindb

LaminDB

Overview

LaminDB is an open-source data framework for biology designed to make data queryable, traceable, reproducible, and FAIR (Findable, Accessible, Interoperable, Reusable). It provides a unified platform that combines lakehouse architecture, lineage tracking, feature stores, biological ontologies, LIMS (Laboratory Information Management System), and ELN (Electronic Lab Notebook) capabilities through a single Python API.

Core Value Proposition:

Queryability: Search and filter datasets by metadata, features, and ontology terms

Traceability: Automatic lineage tracking from raw data through analysis to results

Reproducibility: Version control for data, code, and environment

FAIR Compliance: Standardized annotations using biological ontologies

When to Use This Skill

Use this skill when:

Managing biological datasets: scRNA-seq, bulk RNA-seq, spatial transcriptomics, flow cytometry, multi-modal data, EHR data

Tracking computational workflows: Notebooks, scripts, pipeline execution (Nextflow, Snakemake, Redun)

Curating and validating data: Schema validation, standardization, ontology-based annotation

Working with biological ontologies: Genes, proteins, cell types, tissues, diseases, pathways (via Bionty)

Building data lakehouses: Unified query interface across multiple datasets

Ensuring reproducibility: Automatic versioning, lineage tracking, environment capture

Integrating ML pipelines: Connecting with Weights & Biases, MLflow, HuggingFace, scVI-tools

Deploying data infrastructure: Setting up local or cloud-based data management systems

Collaborating on datasets: Sharing curated, annotated data with standardized metadata

Core Capabilities

LaminDB provides six interconnected capability areas, each documented in detail in the references folder.

1. Core Concepts and Data Lineage

Core entities:

Artifacts: Versioned datasets (DataFrame, AnnData, Parquet, Zarr, etc.)

Records: Experimental entities (samples, perturbations, instruments)

Runs & Transforms: Computational lineage tracking (what code produced what data)

Features: Typed metadata fields for annotation and querying

Key workflows:

Create and version artifacts from files or Python objects

Track notebook/script execution with ln.track() and ln.finish()

Annotate artifacts with typed features

Visualize data lineage graphs with artifact.view_lineage()

Query by provenance (find all outputs from specific code/inputs)

Reference: references/core-concepts.md - Read this for detailed information on artifacts, records, runs, transforms, features, versioning, and lineage tracking.

2. Data Management and Querying

Query capabilities:

Registry exploration and lookup with auto-complete

Single record retrieval with get(), one(), one_or_none()

Filtering with comparison operators (__gt, __lte, __contains, __startswith)

Feature-based queries (query by annotated metadata)

Cross-registry traversal with double-underscore syntax

Full-text search across registries

Advanced logical queries with Q objects (AND, OR, NOT)

Streaming large datasets without loading into memory

Key workflows:

Browse artifacts with filters and ordering

Query by features, creation date, creator, size, etc.

Stream large files in chunks or with array slicing

Organize data with hierarchical keys

Group artifacts into collections

Reference: references/data-management.md - Read this for comprehensive query patterns, filtering examples, streaming strategies, and data organization best practices.

3. Annotation and Validation

Curation process:

Validation: Confirm datasets match desired schemas

Standardization: Fix typos, map synonyms to canonical terms

Annotation: Link datasets to metadata entities for queryability

Schema types:

Flexible schemas: Validate only known columns, allow additional metadata

Minimal required schemas: Specify essential columns, permit extras

Strict schemas: Complete control over structure and values

Supported data types:

DataFrames (Parquet, CSV)

AnnData (single-cell genomics)

MuData (multi-modal)

SpatialData (spatial transcriptomics)

TileDB-SOMA (scalable arrays)

Key workflows:

Define features and schemas for data validation

Use DataFrameCurator or AnnDataCurator for validation

Standardize values with .cat.standardize()

Map to ontologies with .cat.add_ontology()

Save curated artifacts with schema linkage

Query validated datasets by features

Reference: references/annotation-validation.md - Read this for detailed curation workflows, schema design patterns, handling validation errors, and best practices.

4. Biological Ontologies

Available ontologies (via Bionty):

Genes (Ensembl), Proteins (UniProt)

Cell types (CL), Cell lines (CLO)

Tissues (Uberon), Diseases (Mondo, DOID)

Phenotypes (HPO), Pathways (GO)

Experimental factors (EFO), Developmental stages

Organisms (NCBItaxon), Drugs (DrugBank)

Key workflows:

Import public ontologies with bt.CellType.import_source()

Search ontologies with keyword or exact matching

Standardize terms using synonym mapping

Explore hierarchical relationships (parents, children, ancestors)

Validate data against ontology terms

Annotate datasets with ontology records

Create custom terms and hierarchies

Handle multi-organism contexts (human, mouse, etc.)

Reference: references/ontologies.md - Read this for comprehensive ontology operations, standardization strategies, hierarchy navigation, and annotation workflows.

5. Integrations

Workflow managers:

Nextflow: Track pipeline processes and outputs

Snakemake: Integrate into Snakemake rules

Redun: Combine with Redun task tracking

MLOps platforms:

Weights & Biases: Link experiments with data artifacts

MLflow: Track models and experiments

HuggingFace: Track model fine-tuning

scVI-tools: Single-cell analysis workflows

Storage systems:

Local filesystem, AWS S3, Google Cloud Storage

S3-compatible (MinIO, Cloudflare R2)

HTTP/HTTPS endpoints (read-only)

HuggingFace datasets

Array stores:

TileDB-SOMA (with cellxgene support)

DuckDB for SQL queries on Parquet files

Visualization:

Vitessce for interactive spatial/single-cell visualization

Version control:

Git integration for source code tracking

Reference: references/integrations.md - Read this for integration patterns, code examples, and troubleshooting for third-party systems.

6. Setup and Deployment

Installation:

Basic: uv pip install lamindb

With extras: uv pip install 'lamindb[gcp,zarr,fcs]'

Modules: bionty, wetlab, clinical

Instance types:

Local SQLite (development)

Cloud storage + SQLite (small teams)

Cloud storage + PostgreSQL (production)

Storage options:

Local filesystem

AWS S3 with configurable regions and permissions

Google Cloud Storage

S3-compatible endpoints (MinIO, Cloudflare R2)

Configuration:

Cache management for cloud files

Multi-user system configurations

Git repository sync

Environment variables

Deployment patterns:

Local dev → Cloud production migration

Multi-region deployments

Shared storage with personal instances

Reference: references/setup-deployment.md - Read this for detailed installation, configuration, storage setup, database management, security best practices, and troubleshooting.

Common Use Case Workflows

Use Case 1: Single-Cell RNA-seq Analysis with Ontology Validation

import lamindb as ln
import bionty as bt
import anndata as ad
Start tracking

ln.track(params={"analysis": "scRNA-seq QC and annotation"})
Import cell type ontology

bt.CellType.import_source()
Load data

adata = ad.read_h5ad("raw_counts.h5ad")
Validate and standardize cell types

adata.obs["cell_type"] = bt.CellType.standardize(adata.obs["cell_type"])
Curate with schema

curator = ln.curators.AnnDataCurator(adata, schema)
curator.validate()
artifact = curator.save_artifact(key="scrna/validated.h5ad")
Link ontology annotations

cell_types = bt.CellType.from_values(adata.obs.cell_type)
artifact.feature_sets.add_ontology(cell_types)
ln.finish()

Use Case 2: Building a Queryable Data Lakehouse

import lamindb as ln
Register multiple experiments

for i, file in enumerate(data_files):
    artifact = ln.Artifact.from_anndata(
        ad.read_h5ad(file),
        key=f"scrna/batch_{i}.h5ad",
        description=f"scRNA-seq batch {i}"
    ).save()
    # Annotate with features
    artifact.features.add_values({
        "batch": i,
        "tissue": tissues[i],
        "condition": conditions[i]
    })
Query across all experiments

immune_datasets = ln.Artifact.filter(
    key__startswith="scrna/",
    tissue="PBMC",
    condition="treated"
).to_dataframe()
Load specific datasets

for artifact in immune_datasets:
    adata = artifact.load()
    # Analyze

Use Case 3: ML Pipeline with W&B Integration

import lamindb as ln
import wandb
Initialize both systems

wandb.init(project="drug-response", name="exp-42")
ln.track(params={"model": "random_forest", "n_estimators": 100})
Load training data from LaminDB

train_artifact = ln.Artifact.get(key="datasets/train.parquet")
train_data = train_artifact.load()
Train model

model = train_model(train_data)
Log to W&B

wandb.log({"accuracy": 0.95})
Save model in LaminDB with W&B linkage

import joblib
joblib.dump(model, "model.pkl")
model_artifact = ln.Artifact("model.pkl", key="models/exp-42.pkl").save()
model_artifact.features.add_values({"wandb_run_id": wandb.run.id})
ln.finish()
wandb.finish()

Use Case 4: Nextflow Pipeline Integration

# In Nextflow process script
import lamindb as ln
ln.track()
Load input artifact

input_artifact = ln.Artifact.get(key="raw/batch_${batch_id}.fastq.gz")
input_path = input_artifact.cache()
Process (alignment, quantification, etc.)

... Nextflow process logic ...
Save output

output_artifact = ln.Artifact(
    "counts.csv",
    key="processed/batch_${batch_id}_counts.csv"
).save()
ln.finish()

Getting Started Checklist

To start using LaminDB effectively:

Installation & Setup (references/setup-deployment.md)

- Install LaminDB and required extras
- Authenticate with lamin login
- Initialize instance with lamin init --storage ...

Learn Core Concepts (references/core-concepts.md)

- Understand Artifacts, Records, Runs, Transforms
- Practice creating and retrieving artifacts
- Implement ln.track() and ln.finish() in workflows

Master Querying (references/data-management.md)

- Practice filtering and searching registries
- Learn feature-based queries
- Experiment with streaming large files

Set Up Validation (references/annotation-validation.md)

- Define features relevant to research domain
- Create schemas for data types
- Practice curation workflows

Integrate Ontologies (references/ontologies.md)

- Import relevant biological ontologies (genes, cell types, etc.)
- Validate existing annotations
- Standardize metadata with ontology terms

Connect Tools (references/integrations.md)

- Integrate with existing workflow managers
- Link ML platforms for experiment tracking
- Configure cloud storage and compute

Key Principles

Follow these principles when working with LaminDB:

Track everything: Use ln.track() at the start of every analysis for automatic lineage capture

Validate early: Define schemas and validate data before extensive analysis

Use ontologies: Leverage public biological ontologies for standardized annotations

Organize with keys: Structure artifact keys hierarchically (e.g., project/experiment/batch/file.h5ad)

Query metadata first: Filter and search before loading large files

Version, don't duplicate: Use built-in versioning instead of creating new keys for modifications

Annotate with features: Define typed features for queryable metadata

Document thoroughly: Add descriptions to artifacts, schemas, and transforms

Leverage lineage: Use view_lineage() to understand data provenance

Start local, scale cloud: Develop locally with SQLite, deploy to cloud with PostgreSQL

Reference Files

This skill includes comprehensive reference documentation organized by capability:

references/core-concepts.md - Artifacts, records, runs, transforms, features, versioning, lineage

references/data-management.md - Querying, filtering, searching, streaming, organizing data

references/annotation-validation.md - Schema design, curation workflows, validation strategies

references/ontologies.md - Biological ontology management, standardization, hierarchies

references/integrations.md - Workflow managers, MLOps platforms, storage systems, tools

references/setup-deployment.md - Installation, configuration, deployment, troubleshooting

Read the relevant reference file(s) based on the specific LaminDB capability needed for the task at hand.

Additional Resources

Official Documentation: https://docs.lamin.ai

API Reference: https://docs.lamin.ai/api

GitHub Repository: https://github.com/laminlabs/lamindb

Tutorial: https://docs.lamin.ai/tutorial

FAQ: https://docs.lamin.ai/faq

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