Introduction#

LaminDB is an open-source data framework for biology.

Manage storage & databases with a unified Python API (“lakehouse”).
Track data lineage across notebooks & pipelines.
Integrate registries for experimental metadata & in-house ontologies.
Validate, standardize & annotate.
Collaborate across distributed databases.

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

Basic features of LaminHub are free. Enterprise features hosted in your or our infrastructure are available on a paid plan!

Quickstart#

You’ll ingest a small dataset while tracking data lineage, and learn how to validate, annotate, query & search.

Setup#

Install the lamindb Python package:

pip install 'lamindb[jupyter,bionty]'

Initialize a LaminDB instance mounting plugin bionty for biological types.

import lamindb as ln

# artifacts are stored in a local directory `./lamin-intro`
ln.setup.init(schema="bionty", storage="./lamin-intro")

Track#

Run track() to track the input and output data of your code.

# tag your notebook or script with auto-generated identifiers
ln.settings.transform.stem_uid = "FPnfDtJz8qbE"
ln.settings.transform.version = "1"

# track the execution of your code
ln.track()

Artifacts#

Use Artifact to manage data in local or remote storage.

import pandas as pd

# a sample dataset
df = pd.DataFrame(
    {"CD8A": [1, 2, 3], "CD4": [3, 4, 5], "CD14": [5, 6, 7], "perturbation": ["DMSO", "IFNG", "DMSO"]},
    index=["observation1", "observation2", "observation3"],
)

# create an artifact from a DataFrame
artifact = ln.Artifact.from_df(df, description="my RNA-seq", version="1")

# artifacts come with typed, relational metadata
artifact.describe()

# save data & metadata in one operation
artifact.save()

# view data lineage
artifact.view_lineage()

# load an artifact
artifact.load()

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Artifact(uid='6RYYWWSbhNmNc5yDNXan', suffix='.parquet', accessor='DataFrame', description='my RNA-seq', version='1', size=4122, hash='7G4IvBholy73m8bYlfVCrw', hash_type='md5', visibility=1, key_is_virtual=True)

Provenance:
  📎 storage: Storage(uid='kJ1lNRKl', root='/home/runner/work/lamindb/lamindb/docs/lamin-intro', type='local')
  📎 transform: Transform(uid='FPnfDtJz8qbE5zKv', name='Introduction', key='introduction', version='1', type='notebook')
  📎 run: Run(uid='CCpugIlmhwxPBJ7bFomy', started_at=2024-04-22 07:27:47 UTC, is_consecutive=True)
  📎 created_by: User(uid='00000000', handle='anonymous')

_images/e221429f5a3189455b75408c7825f30ccdc6f69e5034c5034cb0d27334c4339f.svg

	CD8A	CD4	CD14	perturbation
observation1	1	3	5	DMSO
observation2	2	4	6	IFNG
observation3	3	5	7	DMSO

Artifacts store datasets or models as files and folders.

Labels#

Label an artifact with a ULabel.

# create a label
candidate_marker_study = ln.ULabel(name="Candidate marker study")
candidate_marker_study.save()

# label an artifact
artifact.labels.add(candidate_marker_study)
artifact.describe()

# the ULabel registry
ln.ULabel.df() 

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Artifact(uid='6RYYWWSbhNmNc5yDNXan', suffix='.parquet', accessor='DataFrame', description='my RNA-seq', version='1', size=4122, hash='7G4IvBholy73m8bYlfVCrw', hash_type='md5', visibility=1, key_is_virtual=True, updated_at=2024-04-22 07:27:48 UTC)

Provenance:
  📎 storage: Storage(uid='kJ1lNRKl', root='/home/runner/work/lamindb/lamindb/docs/lamin-intro', type='local')
  📎 transform: Transform(uid='FPnfDtJz8qbE5zKv', name='Introduction', key='introduction', version='1', type='notebook')
  📎 run: Run(uid='CCpugIlmhwxPBJ7bFomy', started_at=2024-04-22 07:27:47 UTC, is_consecutive=True)
  📎 created_by: User(uid='00000000', handle='anonymous')
Labels:
  📎 ulabels (1, core.ULabel): 'Candidate marker study'

	uid	name	description	reference	reference_type	created_at	updated_at	created_by_id
id
1	gZO0OhRD	Candidate marker study	None	None	None	2024-04-22 07:27:48.563378+00:00	2024-04-22 07:27:48.563407+00:00	1

Queries#

Write arbitrary relational queries (under-the-hood, LaminDB is SQL & Django).

# get an entity by uid
transform = ln.Transform.get("FPnfDtJz8qbE")

# filter by description
ln.Artifact.filter(description="my RNA-seq").df()

# query all artifacts ingested from a notebook named "Introduction"
artifacts = ln.Artifact.filter(transform__name="Introduction").all()

# query all artifacts ingested from a notebook with "intro" in the name and labeled "Candidate marker study"
artifacts = ln.Artifact.filter(transform__name__icontains="intro", ulabels=candidate_marker_study).all()

Search#

# search in a registry
ln.Transform.search("intro")

# look up records with auto-complete
labels = ln.ULabel.lookup()

Validate & annotate#

In LaminDB, validation & annotation of categoricals are closely related by mapping categories on registry content.

Let’s validate a DataFrame by passing validation criteria while constructing an Annotate flow object.

Validate#

# construct an object to validate & annotate a DataFrame
annotate = ln.Annotate.from_df(
    df,
    # define validation criteria
    columns=ln.Feature.name,  # map column names
    categoricals={df.perturbation.name: ln.ULabel.name},  # map categories
)

# the dataframe doesn't validate because registries don't contain the identifiers
annotate.validate()

Update registries#

# add non-validated identifiers to their mapped registries
annotate.add_new_from_columns()
annotate.add_new_from(df.perturbation.name)

# the registered labels & features that will from now on be used for validation
ln.ULabel.df()
ln.Feature.df()

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✅ added 3 records with Feature.name for columns: ['CD14', 'CD4', 'CD8A']

✅ added 2 records with ULabel.name for perturbation: ['DMSO', 'IFNG']

	uid	name	type	unit	description	registries	synonyms	created_at	updated_at	created_by_id
id
4	OwqPDmyM267y	CD14	number	None	None	None	None	2024-04-22 07:27:48.864769+00:00	2024-04-22 07:27:48.864783+00:00	1
3	vuXnWaNfVxwd	CD4	number	None	None	None	None	2024-04-22 07:27:48.864644+00:00	2024-04-22 07:27:48.864658+00:00	1
2	7zcu18vDzRsE	CD8A	number	None	None	None	None	2024-04-22 07:27:48.864494+00:00	2024-04-22 07:27:48.864523+00:00	1
1	3yPqKJDGyWDZ	perturbation	category	None	None	None	None	2024-04-22 07:27:48.685009+00:00	2024-04-22 07:27:48.685042+00:00	1

Annotate#

# given the updated registries, the validation passes
annotate.validate()

# save annotated artifact
artifact = annotate.save_artifact(description="my RNA-seq", version="1")
artifact.describe()

Query for annotations#

ulabels = ln.ULabel.lookup()
ln.Artifact.filter(ulabels=ulabels.ifng).one()

Biological registries#

The generic Feature and ULabel registries will get you pretty far.

But let’s now look at what you do can with a dedicated biological registry like Gene.

Access public ontologies#

Every bionty registry is based on configurable public ontologies.

import bionty as bt

cell_types = bt.CellType.public()
cell_types

cell_types.search("gamma delta T cell").head(2)

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	ontology_id	definition	synonyms	parents	__ratio__
name
gamma-delta T cell	CL:0000798	A T Cell That Expresses A Gamma-Delta T Cell R...	gamma-delta T lymphocyte\|gamma-delta T-lymphoc...	[CL:0000084]	100.000000
CD27-negative gamma-delta T cell	CL:0002125	A Circulating Gamma-Delta T Cell That Expresse...	gammadelta-17 cells	[CL:0000800]	86.486486

Validate & annotate with typed features#

import anndata as ad

# store the dataset as an AnnData object to distinguish data from metadata
adata = ad.AnnData(df[["CD8A", "CD4", "CD14"]], obs=df[["perturbation"]])

# create an annotation flow for an AnnData object
annotate = ln.Annotate.from_anndata(
    adata,
    # define validation criteria
    var_index=bt.Gene.symbol, # map .var.index onto Gene registry
    categoricals={adata.obs.perturbation.name: ln.ULabel.name}, 
    organism="human",  # specify the organism for the Gene registry
)
annotate.validate()

# save annotated artifact
artifact = annotate.save_artifact(description="my RNA-seq", version="1")
artifact.describe()

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✅ added 3 records from public with Gene.symbol for var_index: ['CD8A', 'CD4', 'CD14']

✅ var_index is validated against Gene.symbol

✅ perturbation is validated against ULabel.name

💡 path content will be copied to default storage upon `save()` with key `None` ('.lamindb/re5vbi0B4ccbeFXVicNc.h5ad')

✅ storing artifact 're5vbi0B4ccbeFXVicNc' at '/home/runner/work/lamindb/lamindb/docs/lamin-intro/.lamindb/re5vbi0B4ccbeFXVicNc.h5ad'

💡 parsing feature names of X stored in slot 'var'

✅    3 terms (100.00%) are validated for symbol

✅    linked: FeatureSet(uid='nwPITRpzTV6F2YQLqpHl', n=3, type='number', registry='bionty.Gene', hash='f2UVeHefaZxXFjmUwo9O', created_by_id=1)

💡 parsing feature names of slot 'obs'

✅    1 term (100.00%) is validated for name

✅    linked: FeatureSet(uid='7rvcsKYfyCLXGVgckGSV', n=1, registry='core.Feature', hash='q1hBWV7j1AqZwyLi5ZXU', created_by_id=1)

✅ saved 2 feature sets for slots: 'var','obs'

Artifact(uid='re5vbi0B4ccbeFXVicNc', suffix='.h5ad', accessor='AnnData', description='my RNA-seq', version='1', size=19240, hash='ohAeiVMJZOrc3bFTKmankw', hash_type='md5', n_observations=3, visibility=1, key_is_virtual=True, updated_at=2024-04-22 07:27:54 UTC)

Provenance:
  📎 storage: Storage(uid='kJ1lNRKl', root='/home/runner/work/lamindb/lamindb/docs/lamin-intro', type='local')
  📎 transform: Transform(uid='FPnfDtJz8qbE5zKv', name='Introduction', key='introduction', version='1', type='notebook')
  📎 run: Run(uid='CCpugIlmhwxPBJ7bFomy', started_at=2024-04-22 07:27:47 UTC, is_consecutive=True)
  📎 created_by: User(uid='00000000', handle='anonymous')
Features:
  var: FeatureSet(uid='nwPITRpzTV6F2YQLqpHl', n=3, type='number', registry='bionty.Gene')
    'CD14', 'CD4', 'CD8A'
  obs: FeatureSet(uid='7rvcsKYfyCLXGVgckGSV', n=1, registry='core.Feature')
    🔗 perturbation (2, core.ULabel): 'IFNG', 'DMSO'
Labels:
  📎 ulabels (2, core.ULabel): 'IFNG', 'DMSO'

Query for typed features#

# get a lookup object for human genes
genes = bt.Gene.filter(organism__name="human").lookup()
# query for all feature sets that contain CD8A
feature_sets = ln.FeatureSet.filter(genes=genes.cd8a).all()
# write the query
ln.Artifact.filter(feature_sets__in=feature_sets).df()

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	uid	storage_id	key	suffix	accessor	description	version	size	hash	hash_type	n_objects	n_observations	transform_id	run_id	visibility	key_is_virtual	created_at	updated_at	created_by_id
id
2	re5vbi0B4ccbeFXVicNc	1	None	.h5ad	AnnData	my RNA-seq	1	19240	ohAeiVMJZOrc3bFTKmankw	md5	None	3	1	1	1	True	2024-04-22 07:27:54.810812+00:00	2024-04-22 07:27:54.858798+00:00	1

Add new records#

Create a cell type record and add a new cell state.

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

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✅ created 1 CellType record from Bionty matching name: 'neuron'

💡 also saving parents of CellType(uid='3QnZfoBk', name='neuron', ontology_id='CL:0000540', synonyms='nerve cell', description='The Basic Cellular Unit Of Nervous Tissue. Each Neuron Consists Of A Body, An Axon, And Dendrites. Their Purpose Is To Receive, Conduct, And Transmit Impulses In The Nervous System.', updated_at=2024-04-22 07:27:55 UTC, public_source_id=21, created_by_id=1)

✅ created 3 CellType records from Bionty matching ontology_id: 'CL:0002319', 'CL:0000404', 'CL:0000393'

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

💡 you can switch this off via: bt.settings.auto_save_parents = False

💡 also saving parents of CellType(uid='7kYbAaTq', name='neural cell', ontology_id='CL:0002319', description='A Cell That Is Part Of The Nervous System.', updated_at=2024-04-22 07:27:56 UTC, public_source_id=21, created_by_id=1)

✅ created 1 CellType record from Bionty matching ontology_id: 'CL:0002371'

💡 also saving parents of CellType(uid='1ivhjCnJ', name='somatic cell', ontology_id='CL:0002371', description='A Cell Of An Organism That Does Not Pass On Its Genetic Material To The Organism'S Offspring (I.E. A Non-Germ Line Cell).', updated_at=2024-04-22 07:27:57 UTC, public_source_id=21, created_by_id=1)

✅ created 1 CellType record from Bionty matching ontology_id: 'CL:0000548'

💡 also saving parents of CellType(uid='2FCw82aH', name='animal cell', ontology_id='CL:0000548', synonyms='metazoan cell', description='A Native Cell That Is Part Of Some Metazoa.', updated_at=2024-04-22 07:27:58 UTC, public_source_id=21, created_by_id=1)

✅ created 1 CellType record from Bionty matching ontology_id: 'CL:0000255'

💡 also saving parents of CellType(uid='4WnpvUTH', name='eukaryotic cell', ontology_id='CL:0000255', updated_at=2024-04-22 07:27:59 UTC, public_source_id=21, created_by_id=1)

✅ created 1 CellType record from Bionty matching ontology_id: 'CL:0000003'

💡 also saving parents of CellType(uid='1AtefLrT', name='native cell', ontology_id='CL:0000003', description='A Cell That Is Found In A Natural Setting, Which Includes Multicellular Organism Cells 'In Vivo' (I.E. Part Of An Organism), And Unicellular Organisms 'In Environment' (I.E. Part Of A Natural Environment).', updated_at=2024-04-22 07:28:00 UTC, public_source_id=21, created_by_id=1)

✅ created 1 CellType record from Bionty matching ontology_id: 'CL:0000000'

💡 also saving parents of CellType(uid='2qSJYeQX', name='electrically responsive cell', ontology_id='CL:0000393', description='A Cell Whose Function Is Determined By Its Response To An Electric Signal.', updated_at=2024-04-22 07:27:56 UTC, public_source_id=21, created_by_id=1)

✅ created 1 CellType record from Bionty matching ontology_id: 'CL:0000211'

💡 also saving parents of CellType(uid='590vrK18', name='electrically active cell', ontology_id='CL:0000211', description='A Cell Whose Function Is Determined By The Generation Or The Reception Of An Electric Signal.', updated_at=2024-04-22 07:28:02 UTC, public_source_id=21, created_by_id=1)

💡 also saving parents of CellType(uid='5NqNmmSr', name='electrically signaling cell', ontology_id='CL:0000404', description='A Cell That Initiates An Electrical Signal And Passes That Signal To Another Cell.', updated_at=2024-04-22 07:27:56 UTC, public_source_id=21, created_by_id=1)

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

# express that it's a neuron state
new_cell_state.parents.add(neuron)

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

_images/398f04f27aa3b02ffd807b663c1069c5695782e4e6da61cd2357d0047da5debb.svg

Scale up data & learning#

How do you learn from new datasets that extend your previous data history? Leverage Collection.

# a new dataset
df = pd.DataFrame(
    {
        "CD8A": [2, 3, 3],
        "CD4": [3, 4, 5],
        "CD38": [4, 2, 3],
        "perturbation": ["DMSO", "IFNG", "IFNG"]
    },
    index=["observation4", "observation5", "observation6"],
)
adata = ad.AnnData(df[["CD8A", "CD4", "CD38"]], obs=df[["perturbation"]])

# validate, annotate and save a new artifact
annotate = ln.Annotate.from_anndata(
    adata,
    var_index=bt.Gene.symbol,
    categoricals={adata.obs.perturbation.name: ln.ULabel.name},
    organism="human"
)
annotate.validate()
artifact2 = annotate.save_artifact(description="my RNA-seq dataset 2")

Collections of artifacts#

Create a collection using Collection.

collection = ln.Collection([artifact, artifact2], name="my RNA-seq collection", version="1")
collection.save(transfer_labels=True)  # transfer labels from artifacts to collection
collection.describe()
collection.view_lineage()

# if it's small enough, you can load the entire collection into memory as if it was one
collection.load()

# typically, it's too big, hence, iterate over its artifacts
collection.artifacts.all()

# or look at a DataFrame listing the artifacts
collection.artifacts.df()

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	uid	storage_id	key	suffix	accessor	description	version	size	hash	hash_type	n_objects	n_observations	transform_id	run_id	visibility	key_is_virtual	created_at	updated_at	created_by_id
id
2	re5vbi0B4ccbeFXVicNc	1	None	.h5ad	AnnData	my RNA-seq	1	19240	ohAeiVMJZOrc3bFTKmankw	md5	None	3	1	1	1	True	2024-04-22 07:27:54.810812+00:00	2024-04-22 07:27:54.858798+00:00	1
3	ypX0cQVlWwSvCAqg4F8C	1	None	.h5ad	AnnData	my RNA-seq dataset 2	None	19240	L37UPl4IUH20HkIRzvlRMw	md5	None	3	1	1	1	True	2024-04-22 07:28:06.496422+00:00	2024-04-22 07:28:06.539994+00:00	1

Data loaders#

# to train models, batch iterate through the collection as if it was one array
from torch.utils.data import DataLoader, WeightedRandomSampler
dataset = collection.mapped(obs_keys=["perturbation"])
sampler = WeightedRandomSampler(
    weights=dataset.get_label_weights("perturbation"), num_samples=len(dataset)
)
data_loader = DataLoader(dataset, batch_size=2, sampler=sampler)
for batch in data_loader:
    pass

Read this blog post for more on training models on sharded datasets.

Data lineage#

Save notebooks & scripts#

If you call finish(), you save the run report, source code, and compute environment to your default storage location.

ln.finish()

See an example for this introductory notebook here.

If you want to cache a notebook or script, call:

lamin get https://lamin.ai/laminlabs/lamindata/transform/FPnfDtJz8qbE5zKv

Data lineage across entire projects#

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 an artifact:

artifact.view_lineage()

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

Distributed databases#

Easily create & access databases#

LaminDB is a distributed system like git. Similar to cloning a repository, collaborators can connect to your instance via:

ln.connect("account-handle/instance-name")

Or you load an instance on the command line for auto-connecting in a Python session:

lamin load "account-handle/instance-name"

Or you create your new instance:

lamin init --storage ./my-data-folder

Custom schemas and plugins#

LaminDB can be customized & extended with schema & app plugins building on the Django ecosystem. Examples are:

bionty: Registries for basic biological entities, coupled to public ontologies.
wetlab: 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 wetlab
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#

Why?#

The complexity of modern R&D data often blocks realizing the scientific progress it promises.

See this blog post.

Assumptions#

Batched datasets from physical instruments are transformed (Transform) into useful representations (Artifact)
Learning needs features (Feature, CellMarker, …) and labels (ULabel, CellLine, …)
Insights connect representations to experimental metadata and knowledge (ontologies)

Schema & API#

LaminDB provides a SQL schema for common entities: Artifact, Collection, Transform, Feature, ULabel etc. - see the API reference or the source code.

The core schema is extendable through plugins (see blue vs. red entities in graphic), e.g., with basic biological (Gene, Protein, CellLine, etc.) & operational entities (Biosample, Techsample, Treatment, etc.).

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

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.
bionty: Registries for basic biological entities, coupled to public ontologies.
wetlab: An (exemplary) wetlab schema.
guides: Guides.
usecases: Use cases.

LaminHub is not open-sourced.

Influences#

LaminDB was influenced by many other projects, see Influences.