LaminDB: Manage R&D data & analyses.
Project description
LaminDB: Manage R&D data & analyses
Curate, store, track, query, integrate, and learn from biological data.
LaminDB is an open-source data lake for R&D in biology. It manages indexed object storage (local directories, S3, GCP) with a mapped SQL database (SQLite, Postgres, and soon, BigQuery).
One cool thing is that 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.
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
If you need to work with bionty (feature parsing) and wetlab schemas:
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>
:::{dropdown} Example code
lamin signup testuser1@lamin.ai
lamin login testuser1
lamin init --storage ./mydata --schema bionty,wetlab
:::
See {doc}/guide/setup for more.
Track & query data
Track data sources, data, and metadata
::::{tab-set} :::{tab-item} Within an interactive notebook
import lamindb as ln
ln.Run() # data source (a run record) is created
#> ℹ️ Instance: testuser1/mydata
#> ℹ️ User: testuser1
#> ℹ️ Loaded notebook: Notebook(id='OdlFhFWW7qg3', v='0', name='04-memory', title='Track in-memory data objects', created_by='DzTjkKse', created_at=datetime.datetime(2023, 3, 15, 16, 14, 42))
#> ℹ️ Loaded run:
#> Run(id='L1oBMKW60ndt5YtjRqav', notebook_id='sePTpDsGJRq3', notebook_v='0', created_by='bKeW4T6E', created_at=datetime.datetime(2023, 3, 14, 21, 49, 36))
df = pd.DataFrame({"a": [1, 2], "b": [3, 4]})
# create a data object with SQL metadata record including hash
# link run record
dobject = ln.DObject(df, name="My dataframe")
#> DObject(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 DObject record to the SQL database
ln.add(dobject)
#> DObject(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))
::: :::{tab-item} Within a regular pipeline
# create (or query) a pipeline record
pipeline = lns.Pipeline(name="My pipeline")
#> Pipeline(id='fhn5Zydf', v='1', name='My 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.pipeline)
#> Pipeline(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
dobject = ln.DObject(df, name="My dataframe", source=run)
#> DObject(id='dZvGD7YUKCKG4X4aLd5K', name='My dataframe', suffix='.parquet', size=2240, hash='R2_kKlH1nBGesMdyulMYkA', source_id='L1oBMKW60ndt5YtjRqav', storage_id='wor0ul6c')
# Tip: If you work with a single thread, you can pass `global_context=True` to ln.Run(), allowing you to omit source=run
# upload serialized version to the configured storage
# commit a DObject record to the SQL database
ln.add(dobject)
#> DObject(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
dobject = ln.select(ln.DObject, name="My dataframe").one()
#> [DObject(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 = dobject.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))
dobject = ln.select(ln.DObject).where(ln.DObject.source == run).all()
#> [DObject(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 {doc}/guide/track 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
dobject = ln.DObject(adata, name="Mouse Lymph Node scRNA-seq", features=features)
# access linked gene references
print(dobject.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 DObject record to the SQL database
# commit all linked features to the SQL database
ln.add(dobject)
#> DObject(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 {doc}/guide/features for more.
- Each page in this guide is a Jupyter Notebook, which you can download [here](https://github.com/laminlabs/lamindb/tree/main/docs/guide).
- You can run these notebooks in hosted versions of JupyterLab, e.g., [Saturn Cloud](https://github.com/laminlabs/run-lamin-on-saturn), Google Vertex AI, and others.
- We recommend using [JupyterLab](https://jupyterlab.readthedocs.io/) for best notebook tracking experience.
📬 Reach out to report issues, learn about data modules that connect your assays, pipelines & workflows within our data platform enterprise plan.
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