lamindb 0.49.0

Open-source data lake & feature store for biology.

LaminDB

Open-source data lake & feature store for biology.

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

Update 2023-06-14:

- We completed a major migration from SQLAlchemy/SQLModel to Django, available in 0.42.0.
- The last version before the migration is 0.41.2.

Introduction

LaminDB is an open-source Python library to:

• Manage files & datasets while tracking provenance across pipelines, notebooks & app uploads.
• Manage biological registries, ontologies, features & schemas.
• Enhance integrity through built-in data validation and idempotent, ACID operations.
• Query, search, look up, save, load and stream with one API.
• Collaborate across a mesh of LaminDB instances.

LaminApp is a data management app built on LaminDB. If LaminDB ~ git, LaminApp ~ GitHub.

LaminApp, support, code templates & auto-dispatched integration tests for a BioTech data & analytics platform are currently only available on an enterprise plan. LaminApp is available for your cloud infrastructure or hosted by us.

Quickstart

Installation and sign-up take no time: Run pip install lamindb and lamin signup <email> on the command line.

Then, init a LaminDB instance with local or cloud default storage like you'd init a git repository:

$ lamin init --storage ./mydata   # or s3://my-bucket, gs://my-bucket

Import lamindb:

import lamindb as ln
# import lnschema_bionty as lb # optional, for bionty schema

Manage data objects

# Store a DataFrame object
df = pd.DataFrame({"feat1": [1, 2], "feat2": [3, 4]})  # AnnData works, too
ln.File(df, description="Data batch 1").save()  # create a File object and save/upload it

# To find it, if you don't have specific metadata in mind, run a search
ln.File.search("batch 1")
# Or filter (under-the-hood, you have the full power of SQL to query)
file = ln.File.filter(description="Data batch 1").one()  # get exactly one result

# Load a file back into memory
df = file.load()
# Or get a backed accessor to stream its content from the cloud:
backed = file.backed()  # currently works for AnnData, zarr, HDF5, not yet for DataFrame

Manage files

# Store a file
file = ln.File("s3://my-bucket/images/image001.jpg")  # or a local path
file.save()  # register the file

# Query by `key` (the relative path within your storage) and load into memory
file.filter(key__startswith="images/").df()  # all files in folder "images/" in default storage

Auto-complete categoricals and search

# When you're unsure about spellings, use a lookup object:
users = ln.User.lookup()
ln.File.filter(created_by=users.lizlemon)

# Or search
ln.User.search("liz lemon", field="name")
user = ln.User.search("liz lemon", return_queryset=True).first() # grab the top search result as a record

Track & query data lineage

In addition to basic provenance information (created_by, created_at, created_by), you can track which notebooks & pipelines transformed files.

View all parent transforms and files in a lineage graph:

file.view_lineage()

Notebooks

Track a Jupyter Notebook:

ln.track()  # auto-detect & save notebook metadata
ln.File("my_artifact.parquet").save()  # this file is now aware that it was saved in this notebook

When you query the file, later on, you'll know from which notebook it came:

file = ln.File.filter(description="my_artifact.parquet").one()  # query for a file
file.transform  # the notebook with id, title, filename, version, etc.
file.run  # the specific run of the notebook that created the file

# Alternatively, you can query for notebooks and find the files written by them
transforms = ln.Transform.filter(type="notebook", created_at__year=2022).search("T cell").all()
ln.File.filter(transform__in=transforms).df()  # the files created by these notebooks

Pipelines

This works like for notebooks just that you need to provide pipeline metadata yourself.

# To save a pipeline to the `Transform` registry, call
ln.Transform(name="Awesom-O", version="0.41.2").save()  # save a pipeline, optionally with metadata

# Track a pipeline run
transform = ln.Transform.filter(name="Awesom-O", version="0.41.2").one()  # filter pipeline from the registry
ln.track(transform)  # create a new global run context
ln.File("s3://my_samples01/my_artifact.fastq.gz").save()  # file gets auto-linked against run & transform

# Now, you can query for the latest pipeline runs
ln.Run.filter(transform=transform).order_by("-created_at").df()  # get the latest pipeline runs

Load your instance from anywhere

If provided with access, others can load your instance via:

$ lamin load myaccount/mydata

Manage biological registries

$ lamin init --storage ./bioartifacts --schema bionty

# create an ontology-coupled record and save it
lb.CellType.from_bionty(name="neuron").save()

# bulk create knowledge-coupled records
adata = ln.dev.datasets.anndata_with_obs()
cell_types = lb.CellType.from_values(adata.obs.cell_type, field=lb.CellType.name)
ln.save(cell_types) # bulk save cell types

# standardize synonyms
lb.CellType.map_synonyms(["T cell", "T-cell", "T lymphocyte"])

# view ontological hierarchy of a record
neuron = lb.CellType.lookup().neuron
neuron.view_parents(distance=3)

Track biological features

# track features present in var(X) and obs
adata = ln.dev.datasets.anndata_with_obs()
file = ln.File.from_anndata(
    adata, description="my RNA-seq dataset", var_ref=lb.Gene.ensembl_gene_id
)
file.save()

# view a summary of tracked features
# you have registered two feature sets: 'obs' and 'var'
file.features

# add labels to features
tissues = lb.Tissue.from_values(adata.obs["tissue"], field=lb.Tissue.name)
diseases = lb.Disease.from_values(adata.obs["disease"], field=lb.Disease.name)
file.features.add_labels(tissues + diseases)

# fetch labels of a feature
file.features["obs"].get(name="tissue").df()

# display rich metadata of a file (provenance and features)
file.describe()

Manage custom schemas

1. Create a GitHub repository with Django ORMs similar to github.com/laminlabs/lnschema-lamin1
2. Create & deploy migrations via lamin migrate create and lamin migrate deploy

It's fastest if we do this for you based on our templates within an enterprise plan, but you can fully manage the process yourself.

Setup

Installation

pip install lamindb  # basic data management

You can configure the installation using extras, e.g.,

pip install 'lamindb[jupyter,bionty,fcs,aws]'

Supported extras are:

jupyter  # Track Jupyter notebooks
bionty   # Manage basic biological entities
fcs      # Manage FCS files (flow cytometry)
zarr     # Store & stream arrays with zarr
aws      # AWS (s3fs, etc.)
gcp      # Google Cloud (gcfs, etc.)
postgres # Postgres server

Docker

Here is a way of running LaminDB in a docker: github.com/laminlabs/lamindb-docker.

Sign up

Why do I have to sign up?

• Data lineage requires a user identity (who modified which data when?).
• Collaboration requires a user identity (who shares this with me?).

Signing up takes 1 min.

We do not store any of your data, but only basic metadata about you (email address, etc.) & your LaminDB instances (S3 bucket names, etc.).

• Sign up: lamin signup <email>
• Log in: lamin login <handle>

How does it work?

Dependencies

LaminDB builds semantics of R&D and biology onto well-established tools:

• SQLite & Postgres for SQL databases using Django ORM (previously: SQLModel)
• S3, GCP & local storage for object storage using fsspec
• Configurable storage formats: pyarrow, anndata, zarr, etc.
• Biological knowledge sources & ontologies: see Bionty

LaminDB is open source.

Architecture

LaminDB consists of the lamindb Python package (repository here) with its components:

• bionty: Basic biological entities (usable standalone).
• lamindb-setup: Setup & configure LaminDB, client for Lamin Hub.
• lnschema-core: Core schema, ORMs to model data objects & data lineage.
• lnschema-bionty: Bionty schema, ORMs that are coupled to Bionty's entities.
• lnschema-lamin1: Exemplary configured schema to track samples, treatments, etc.
• nbproject: Parse metadata from Jupyter notebooks.
• lamin-utils: Utilities for LaminDB and Bionty.
• readfcs: FCS file reader.

LaminHub & LaminApp are not open-sourced, and neither are templates that model lab operations.

Notebooks

• Find all guide notebooks here.
• You can run these notebooks in hosted versions of JupyterLab, e.g., Saturn Cloud, Google Vertex AI, Google Colab, and others.
• Jupyter Lab & Notebook offer a fully interactive experience, VS Code & others require using the CLI to track notebooks: lamin track my-notebook.ipynb

Documentation

Read the docs.