Introduction#

LaminDB is an open-source Python framework to manage biological data & analyses in generic backends:

Unify access to data & metadata across storage (files, arrays) & database (SQL) backends.
Track data flow across notebooks, pipelines & UI.
Manage registries for experimental metadata & ontologies.
Validate & annotate data batches using in-house & public knowledge.
Organize and share data across a mesh of LaminDB instances.

LaminHub is a data collaboration hub built on LaminDB similar to how GitHub is built on git.

LaminHub is free for public data. Enterprise features, support, integration tests & wetlab plug-ins hosted in your or our infrastructure are available on a paid plan: please reach out!

Quickstart#

Warning

Public beta: Close to having converged a stable API, but some breaking changes might still occur.

Setup#

Sign up for a free account (see more info).
Install the lamindb Python package:
```
pip install 'lamindb[jupyter,bionty]'
```

lamin login <email> --password <password>

You can now init LaminDB instances like you init git repositories, e.g.:

!lamin init --schema bionty --storage ./lamin-intro  # or s3://my-bucket, gs://my-bucket as default storage

Because we passed --schema bionty, this instance mounted plug-in lnschema_bionty.

Register a dataset#

import lamindb as ln
import pandas as pd

# track data flow through current notebook
ln.track()

# access a new batch of data
df = pd.DataFrame(
    {"CD8": [1, 2, 3], "CD45": [3, 4, 5], "perturbation": ["DMSO", "IFNG", "DMSO"]}
)

# create a dataset
dataset = ln.Dataset(df, name="Immune phenotyping 1")
# register dataset
dataset.save()

💡 loaded instance: testuser1/lamin-intro (lamindb 0.54.2)

💡 notebook imports: lamindb==0.54.2 lnschema_bionty==0.31.2 pandas==2.1.1

💡 Transform(id='FPnfDtJz8qbEz8', name='Introduction', short_name='introduction', version='0', type=notebook, updated_at=2023-09-26 15:21:08, created_by_id='DzTjkKse')

💡 Run(id='gjdJnVZYc7ipewoya1rF', run_at=2023-09-26 15:21:08, transform_id='FPnfDtJz8qbEz8', created_by_id='DzTjkKse')

Access a dataset#

# search a dataset
ln.Dataset.search("immune")

# query a dataset
dataset = ln.Dataset.filter(name__contains="phenotyping 1").one()

# view data flow
dataset.view_flow()

# describe metadata
dataset.describe()

# load the dataset
df = dataset.load()

_images/732bbd19d7621fe36fd64382c71305a3ca3a38ce4b227b187e2f0b62be9536f2.svg

Dataset(id='bW2qYmpqAgNW4AbpVY7C', name='Immune phenotyping 1', hash='otKGnMVEtNo6amaaVewBug', updated_at=2023-09-26 15:21:08)

Provenance:
  📔 transform: Transform(id='FPnfDtJz8qbEz8', name='Introduction', short_name='introduction', version='0', type='notebook', updated_at=2023-09-26 15:21:08, created_by_id='DzTjkKse')
  👣 run: Run(id='gjdJnVZYc7ipewoya1rF', run_at=2023-09-26 15:21:08, transform_id='FPnfDtJz8qbEz8', created_by_id='DzTjkKse')
  📄 file: File(id='bW2qYmpqAgNW4AbpVY7C', suffix='.parquet', accessor='DataFrame', description='See dataset bW2qYmpqAgNW4AbpVY7C', size=2913, hash='otKGnMVEtNo6amaaVewBug', hash_type='md5', updated_at=2023-09-26 15:21:08, storage_id='FGbhE5GU', transform_id='FPnfDtJz8qbEz8', run_id='gjdJnVZYc7ipewoya1rF', created_by_id='DzTjkKse')
  👤 created_by: User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-09-26 15:21:04)

Validate & annotate a dataset#

Validate the column names in a DataFrame schema-less:

# define validation criteria
names_types = [("CD8", "number"), ("CD45", "number"), ("perturbation", "category")]

# save validation criteria as features
features = [ln.Feature(name=name, type=type) for (name, type) in names_types]
ln.save(features)

# create dataset & validate features
dataset = ln.Dataset.from_df(df, name="Immune phenotyping 1")
# register dataset & link validated features
dataset.save()

# access linked features
dataset.features

❗ returning existing file with same hash: File(id='bW2qYmpqAgNW4AbpVY7C', suffix='.parquet', accessor='DataFrame', description='See dataset bW2qYmpqAgNW4AbpVY7C', size=2913, hash='otKGnMVEtNo6amaaVewBug', hash_type='md5', updated_at=2023-09-26 15:21:08, storage_id='FGbhE5GU', transform_id='FPnfDtJz8qbEz8', run_id='gjdJnVZYc7ipewoya1rF', created_by_id='DzTjkKse')

❗ returning existing dataset with same hash: Dataset(id='bW2qYmpqAgNW4AbpVY7C', name='Immune phenotyping 1', hash='otKGnMVEtNo6amaaVewBug', updated_at=2023-09-26 15:21:08, transform_id='FPnfDtJz8qbEz8', run_id='gjdJnVZYc7ipewoya1rF', file_id='bW2qYmpqAgNW4AbpVY7C', created_by_id='DzTjkKse')

Features:
  columns: FeatureSet(id='HWLCaLwMcvH9m2c1F91o', n=3, registry='core.Feature', hash='2CWCsmFoutK4ueokkVNU', updated_at=2023-09-26 15:21:08, created_by_id='DzTjkKse')
    perturbation (category)
    CD45 (number)
    CD8 (number)

Use the lnschema_bionty plug-in to type biological entities and validate column names schema-full:

# requires the 'bionty' schema
import lnschema_bionty as lb

# set a global species for multi-species registries
lb.settings.species = "human"

# create cell marker records from the public ontology
cell_markers = [lb.CellMarker.from_bionty(name=name) for name in ["CD8", "CD45"]]
ln.save(cell_markers)

# create dataset & validate features
dataset = ln.Dataset.from_df(
    df.iloc[:, :2], name="Immune phenotyping 2", field=lb.CellMarker.name
)
# register dataset & link validated features
dataset.save()

dataset.features

❗ record with similar name exist! did you mean to load it?

	id	__ratio__
name
Immune phenotyping 1	bW2qYmpqAgNW4AbpVY7C	95.0

Features:
  columns: FeatureSet(id='b9YgiPXnH9EE7Db9n3Ye', n=2, type='number', registry='bionty.CellMarker', hash='VwiLioP9FaLRLPqK7k2c', updated_at=2023-09-26 15:21:11, created_by_id='DzTjkKse')
    'CD45', 'CD8'

Query for annotations#

Query for a panel of cell markers & the linked datasets:

# an object to auto-complete cell markers
cell_markers = lb.CellMarker.lookup()

# all cell marker panel containing CD45
panels_with_cd45 = ln.FeatureSet.filter(cell_markers=cell_markers.cd45).all()

# all datasets measuring CD45
ln.Dataset.filter(feature_sets__in=panels_with_cd45).df()

	name	description	version	hash	reference	reference_type	transform_id	run_id	file_id	initial_version_id	updated_at	created_by_id
id
46soXLHvTyl2rsGnEHLQ	Immune phenotyping 2	None	None	D4h0vbNPI6kBVa2Io8766Q	None	None	FPnfDtJz8qbEz8	gjdJnVZYc7ipewoya1rF	46soXLHvTyl2rsGnEHLQ	None	2023-09-26 15:21:11	DzTjkKse

Annotate with biological labels#

Use the Experimental Factor Ontology to link a validated label for the readout:

# search the public ontology from the bionty store
lb.ExperimentalFactor.bionty().search("facs").head(2)

# create a record for facs
facs = lb.ExperimentalFactor.from_bionty(ontology_id="EFO:0009108")
facs.save()

# label with an inhouse assay
immune_assay1 = lb.ExperimentalFactor(name="Immune phenotyping assay 1")
immune_assay1.save()

dataset.experimental_factors.add(facs, immune_assay1)

# create a tissue from a public ontology
bone_marrow = lb.Tissue.from_bionty(name="bone marrow")
bone_marrow.save()

dataset.tissues.add(bone_marrow)

dataset.describe()

❗ now recursing through parents: this only happens once, but is much slower than bulk saving

Dataset(id='46soXLHvTyl2rsGnEHLQ', name='Immune phenotyping 2', hash='D4h0vbNPI6kBVa2Io8766Q', updated_at=2023-09-26 15:21:11)

Provenance:
  💫 transform: Transform(id='FPnfDtJz8qbEz8', name='Introduction', short_name='introduction', version='0', type=notebook, updated_at=2023-09-26 15:21:11, created_by_id='DzTjkKse')
  👣 run: Run(id='gjdJnVZYc7ipewoya1rF', run_at=2023-09-26 15:21:08, transform_id='FPnfDtJz8qbEz8', created_by_id='DzTjkKse')
  📄 file: File(id='46soXLHvTyl2rsGnEHLQ', suffix='.parquet', accessor='DataFrame', description='See dataset 46soXLHvTyl2rsGnEHLQ', size=2297, hash='D4h0vbNPI6kBVa2Io8766Q', hash_type='md5', updated_at=2023-09-26 15:21:11, storage_id='FGbhE5GU', transform_id='FPnfDtJz8qbEz8', run_id='gjdJnVZYc7ipewoya1rF', created_by_id='DzTjkKse')
  👤 created_by: User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-09-26 15:21:04)
Features:
  columns: FeatureSet(id='b9YgiPXnH9EE7Db9n3Ye', n=2, type='number', registry='bionty.CellMarker', hash='VwiLioP9FaLRLPqK7k2c', updated_at=2023-09-26 15:21:11, created_by_id='DzTjkKse')
    'CD45', 'CD8'
Labels:
  🏷️ tissues (1, bionty.Tissue): 'bone marrow'
  🏷️ experimental_factors (2, bionty.ExperimentalFactor): 'Immune phenotyping assay 1', 'fluorescence-activated cell sorting'

More examples#

Understand data flow#

View the sequence of data transformations (Transform) in a project (from here, based on Schmidt et al., 2022):

transform.view_parents()

Or, the generating flow of a file or dataset:

file.view_flow()

Both figures are based on mere calls to ln.track() in notebooks, pipelines & app.

Manage biological registries#

Create a cell type registry from public knowledge and add a new cell state (from here):

import lnschema_bionty as lb

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

# create a record to track a new cell state
new_cell_state = lb.CellType(name="my neuron cell state", description="explains X")
new_cell_state.save()

# express that it's a neuron state
cell_types = lb.CellType.lookup()
new_cell_state.parents.add(cell_types.neuron)

Show code cell output Hide code cell output

❗ now recursing through parents: this only happens once, but is much slower than bulk saving

❗ records with similar names exist! did you mean to load one of them?

	id	synonyms	__ratio__
name
cell	Ry0JGwSD		90.0
neuron	GCBGsuZM	nerve cell	90.0

# view ontological hierarchy
new_cell_state.view_parents(distance=2)

_images/a66ada3e93da810eb119f6a18980bb56299ab4ba4bb2918d856a2b73347b194d.svg

Leverage a mesh of instances#

LaminDB is a distributed system like git.

For instance, collaborators can load your instance using:

$ lamin load myhandle/myinstance

Manage custom schemas#

LaminDB can be customized & extended with schema & app plug-ins building on the Django ecosystem. Examples are

lnschema_bionty: Registries for basic biological entities, coupled to public ontologies.
lnschema_lamin1: Exemplary custom schema to manage samples, treatments, etc.

If you’d like to create your own schema or app:

Create a git repository with registries similar to lnschema_lamin1
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.

Design#

LaminDB builds semantics of R&D and biology into well-established infrastructure.

It provides a SQL-schema specification for common entities: File, Dataset, Transform, Feature, ULabel etc. - see the API reference or the source code.

On top of the schema, LaminDB is a Python API that abstracts over storage & database access, data transformations, and (biological) ontologies.

The code for this is open-source & accessible through the dependencies & repositories listed below.

Dependencies#

Data is stored in a platform-independent way:
- location → local, on AWS S3 or GCP Storage, accessed through fsspec
- format → blob-like files or queryable formats like Parquet, zarr, HDF5, TileDB & DuckDB
Metadata is stored in SQL: current backends are SQLite (small teams) and Postgres (any team size).
Django ORM for schema management & metadata queries (until v0.41: SQLModel & SQLAlchemy).
Biological knowledge sources & ontologies: see Bionty.

For more details, see the pyproject.toml file in lamindb & the linked repositories below.

Repositories#

LaminDB and its plug-ins consist in open-source Python libraries & publicly hosted metadata assets:

lamindb: Core API, which builds on the core schema.
lnschema-bionty: Registries for basic biological entities, coupled to public ontologies.
lnschema-lamin1: Exemplary custom schema to manage samples, treatments, etc.
lamindb-setup: Setup & configure LaminDB, client for Lamin Hub.
bionty: Accessor for public biological ontologies.
nbproject: Metadata parser for Jupyter notebooks.
lamin-utils: Generic utilities, e.g., a logger.
readfcs: FCS file reader.

LaminHub is not open-sourced, and neither are plug-ins that model lab operations.

Assumptions & principles#

Data is generated by instruments that process physical samples: it comes in batches stored as immutable files.
Files are transformed into more useful data representations, e.g.:
- Summary statistics like count matrices for fastq files
- Array stores of non-array-like input data (e.g., images)
- Higher-level embeddings for lower-level array, text or graph representations
- Concatenated array stores for large-scale atlas-like datasets
Semantics of high-level embeddings (“inflammatory”, “lipophile”) are anchored in experimental metadata and knowledge (ontologies)
Experimental metadata is another ontology type
Experiments measure features (Feature, CellMarker, …)
Samples are annotated by labels (ULabel, CellLine, …)
Learning and data warehousing both iterate data transformations (Transform)
Basic biological entities should have the same meaning to anyone and across any data platform
Schema migrations have to be easy

Influences#

LaminDB was influenced by many other projects, see Influences.

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