lamindb

K-Dense-AI/scientific-agent-skillsBiology Medicine and BioinformaticsAudit passedSnapshot c19ad4023ea9

Installs: 0

Summary

Use when working with LaminDB, the open-source lineage-native lakehouse for biological datasets and models. Covers setup, artifact registration, query/search, lineage tracking, validation, ontology-backed annotation with Bionty, collections, branches, storage, and workflow integrations.

SKILL.md

LaminDB

Overview

LaminDB is an open-source, lineage-native lakehouse for biology. It makes datasets and models queryable, traceable, validated, reproducible, and FAIR (Findable, Accessible, Interoperable, Reusable) while storing data in open formats across local filesystems, S3, GCS, Hugging Face, SQLite, and Postgres.

Core Value Proposition:

Queryability: Search and filter artifacts, records, runs, features, schemas, and collections
Traceability: Track inputs, outputs, parameters, source code, and environments for notebooks, scripts, functions, and pipelines
Validation: Curate DataFrame, AnnData, SpatialData, TileDB-SOMA, Parquet, Zarr, and other biological formats with schemas
FAIR Compliance: Standardize annotations with Bionty-backed ontologies and custom registries
Change management: Organize work with projects, branches, spaces, collections, and saved notes or plans

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, functions, shell scripts, and 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

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

# Start tracking a notebook/script run
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-backed annotations for queryability
cell_types = bt.CellType.from_values(adata.obs["cell_type"])
artifact.cell_types.add(*cell_types)

ln.finish()

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.set_values({
        "batch": i,
        "tissue": tissues[i],
        "condition": conditions[i]
    })

# Query across all experiments by annotated features
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

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.set_values({"wandb_run_id": wandb.run.id})

ln.finish()
wandb.finish()

# 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()

lamindb

Summary

SKILL.md

LaminDB

Overview

When to Use This Skill

Core Capabilities

1. Core Concepts and Data Lineage

2. Data Management and Querying

3. Annotation and Validation

4. Biological Ontologies

5. Integrations

6. Setup and Deployment

Safety and Security Defaults

Common Use Case Workflows

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

Use Case 2: Building a Queryable Data Lakehouse

Use Case 3: ML Pipeline with W&B Integration

Use Case 4: Nextflow Pipeline Integration

Getting Started Checklist

Key Principles

Reference Files

Additional Resources

Related skills