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Guide#

Welcome to the LaminDB guide! 👋

Curate, store, track, query, integrate, and learn from biological data.

LaminDB is an open-source data lake for R&D in biology.

It gives you components to build on data lineage & biological entities with an ORM for your existing infrastructure: object storage (local directories, S3, GCP) with a mapped SQL query engine (SQLite, Postgres, and soon, BigQuery).

You can readily create distributed LaminDB instances at any scale:

  • Get started on your laptop, deploy in the cloud, or work with a mesh of instances for different teams and purposes.

  • Share them through a hub akin to HuggingFace & GitHub - see, e.g, lamin.ai/sunnyosun.

Warning

Public beta: Currently only recommended for collaborators as we still make breaking changes.

Installation#

LaminDB is a python package available for Python versions 3.8+.

pip install lamindb

Biological entities are installed like so:

pip install 'lamindb[bionty,wetlab]'

Import#

In your python script, import LaminDB as:

import lamindb as ln

Quick setup#

Quick setup on the command line:

  • Sign up via lamin signup <email>

  • Log in via lamin login <handle>

  • Set up an instance via lamin init --storage <storage> --schema <schema_modules>

Example code
lamin signup testuser1@lamin.ai
lamin login testuser1
lamin init --storage ./mydata --schema bionty,wetlab

See Setup quickstart for more.

Track & query data#

Track data & metadata with sources#

Track the T in ETL and ELT.

import lamindb as ln

# track global data source (Run & Transform records)
ln.track()
#> ℹ️ Instance: testuser1/mydata
#> ℹ️ User: testuser1
#> ℹ️ Loaded notebook: Transform(id='OdlFhFWW7qg3', v='0', name='04-memory', title='Track in-memory data objects', type=notebook, created_by='DzTjkKse', created_at=datetime.datetime(2023, 3, 15, 16, 14, 42))
#> ℹ️ Loaded run:
#> Run(id='L1oBMKW60ndt5YtjRqav', transform_id='sePTpDsGJRq3', transform_v='0', created_by='bKeW4T6E', created_at=datetime.datetime(2023, 3, 14, 21, 49, 36))

df = pd.DataFrame({"a": [1, 2], "b": [3, 4]})

# serialize data object with SQL metadata record including hash and linked source (run record)
file = ln.File(df, name="My dataframe")
#> File(id='dZvGD7YUKCKG4X4aLd5K', name='My dataframe', suffix='.parquet', size=2240, hash='R2_kKlH1nBGesMdyulMYkA', source_id='L1oBMKW60ndt5YtjRqav', storage_id='wor0ul6c')

# upload serialized version to configured storage
# commit a File record to the SQL database
ln.add(file)
#> File(id='dZvGD7YUKCKG4X4aLd5K', name='My dataframe', suffix='.parquet', size=2240, hash='R2_kKlH1nBGesMdyulMYkA', source_id='L1oBMKW60ndt5YtjRqav', storage_id='wor0ul6c', created_at=datetime.datetime(2023, 3, 14, 21, 49, 46))

# create (or query) a transform record
transform = ln.Transform(name="My pipeline")
#> Transform(id='fhn5Zydf', v='1', name='My pipeline', type=pipeline, created_by='bKeW4T6E')

# create a run from the above pipeline as the data source
run = ln.Run(pipeline=pipeline)
#> Run(id='2aaKWH8dwBE6hnj3n9K9', pipeline_id='fhn5Zydf', pipeline_v='1', created_by='bKeW4T6E')

# access pipeline from run via
print(run.transform)
#> Transform(id='fhn5Zydf', v='1', name='My pipeline', created_by='bKeW4T6E')

df = pd.DataFrame({"a": [1, 2], "b": [3, 4]})

# create a data object with SQL metadata record including hash and link run record
file = ln.File(df, name="My dataframe", source=run)
#> File(id='dZvGD7YUKCKG4X4aLd5K', name='My dataframe', suffix='.parquet', size=2240, hash='R2_kKlH1nBGesMdyulMYkA', source_id='L1oBMKW60ndt5YtjRqav', storage_id='wor0ul6c')

# upload serialized version to the configured storage
# commit a File record to the SQL database
ln.add(file)
#> File(id='dZvGD7YUKCKG4X4aLd5K', name='My dataframe', suffix='.parquet', size=2240, hash='R2_kKlH1nBGesMdyulMYkA', source_id='L1oBMKW60ndt5YtjRqav', storage_id='wor0ul6c', created_at=datetime.datetime(2023, 3, 14, 21, 49, 46))

Query & load data#

file = ln.select(ln.File, name="My dataframe").one()
#> [File(id='dZvGD7YUKCKG4X4aLd5K', name='My dataframe', suffix='.parquet', size=2240, hash='R2_kKlH1nBGesMdyulMYkA', source_id='L1oBMKW60ndt5YtjRqav', storage_id='wor0ul6c', created_at=datetime.datetime(2023, 3, 14, 21, 49, 46))]
df = file.load()
#>      a	b
#>  0	1	3
#>  1	2	4

Get the data ingested by the latest run:

run = ln.select(ln.Run).order_by(ln.Run.created_at.desc()).first()
#> Run(id='L1oBMKW60ndt5YtjRqav', notebook_id='sePTpDsGJRq3', notebook_v='0', created_by='bKeW4T6E', created_at=datetime.datetime(2023, 3, 14, 21, 49, 36))
file = ln.select(ln.File).where(ln.File.source == run).all()
#> [File(id='dZvGD7YUKCKG4X4aLd5K', name='My dataframe', suffix='.parquet', size=2240, hash='R2_kKlH1nBGesMdyulMYkA', source_id='L1oBMKW60ndt5YtjRqav', storage_id='wor0ul6c', created_at=datetime.datetime(2023, 3, 14, 21, 49, 46))]

See Track data for more.

Track biological metadata#

Track biological features#

import bionty as bt  # Lamin's manager for biological knowledge
import lamindb as ln

ln.Run()  # assume we're in a notebook and don't need to pass pipeline_name

# a sample single cell RNA-seq dataset
adata = ln.dev.datasets.anndata_mouse_sc_lymph_node()

# Create a reference
# - ensembl id as the standardized id
# - mouse as the species
reference = bt.Gene(species="mouse")

# parse gene identifiers from data and map on reference
features = ln.Features(adata, reference)
#> 🔶 id column not found, using index as features.
#> ✅ 10000 terms (100.0%) are mapped.
#> 🔶 0 terms (0.0%) are not mapped.
# The result is a hashed feature set record:
print(features)
#> Features(id='2Mv3JtH-ScBVYHilbLaQ', type='gene', created_by='bKeW4T6E')
# genes records can be accessed via:
print(features.genes[:3])
#> [Gene(id='ENSMUSG00000020592', species_id='NCBI_10090'),
#>  Gene(id='ENSMUSG00000034931', species_id='NCBI_10090'),
#>  Gene(id='ENSMUSG00000071005', species_id='NCBI_10090')]

# track data with features
file = ln.File(adata, name="Mouse Lymph Node scRNA-seq", features=features)

# access linked gene references
print(file.features.genes[:3])
#> [Gene(id='ENSMUSG00000020592', species_id='NCBI_10090'),
#>  Gene(id='ENSMUSG00000034931', species_id='NCBI_10090'),
#>  Gene(id='ENSMUSG00000071005', species_id='NCBI_10090')]

# upload serialized data to configured storage
# commit a File record to the SQL database
# commit all linked features to the SQL database
ln.add(file)
#> File(id='VRu0Mg93d5l6NLb4znCD', name='Mouse Lymph Node scRNA-seq', suffix='.h5ad', size=17341245, hash='Qprqj0O23197Ko-VobaZiw', source_id='EB78Sl5KPG6wW6XcOlsm', storage_id='0Xt6BY40', created_at=datetime.datetime(2023, 3, 17, 6, 49, 39))

See Track biological features for more.

Tip

  • Each page in this guide is a Jupyter Notebook, which you can download here.

  • You can run these notebooks in hosted versions of JupyterLab, e.g., Saturn Cloud, Google Vertex AI, and others.

  • We recommend using JupyterLab for best notebook tracking experience.

📬 Reach out to learn about data modules that connect your assays & workflows within our data platform enterprise plan.