Scientific Skill: 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:**
1. **Validation**: Confirm datasets match desired schemas
2. **Standardization**: Fix typos, map synonyms to canonical terms
3. **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

```python
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

```python
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

```python
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

```python
# 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:

1. **Installation & Setup** (`references/setup-deployment.md`)
   - Install LaminDB and required extras
   - Authenticate with `lamin login`
   - Initialize instance with `lamin init --storage ...`

2. **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

3. **Master Querying** (`references/data-management.md`)
   - Practice filtering and searching registries
   - Learn feature-based queries
   - Experiment with streaming large files

4. **Set Up Validation** (`references/annotation-validation.md`)
   - Define features relevant to research domain
   - Create schemas for data types
   - Practice curation workflows

5. **Integrate Ontologies** (`references/ontologies.md`)
   - Import relevant biological ontologies (genes, cell types, etc.)
   - Validate existing annotations
   - Standardize metadata with ontology terms

6. **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:

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

2. **Validate early**: Define schemas and validate data before extensive analysis

3. **Use ontologies**: Leverage public biological ontologies for standardized annotations

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

5. **Query metadata first**: Filter and search before loading large files

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

7. **Annotate with features**: Define typed features for queryable metadata

8. **Document thoroughly**: Add descriptions to artifacts, schemas, and transforms

9. **Leverage lineage**: Use `view_lineage()` to understand data provenance

10. **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