facs3/4 Jupyter Notebook lamindata

Query & integrate data

import lamindb as ln
import bionty as bt
💡 connected lamindb: testuser1/test-facs
ln.settings.transform.stem_uid = "wukchS8V976U"
ln.settings.transform.version = "0"
ln.track()
💡 notebook imports: bionty==0.44.0 lamindb==0.74.0
💡 saved: Transform(uid='wukchS8V976U6K79', version='0', name='Query & integrate data', key='facs3', type='notebook', created_by_id=1, updated_at='2024-06-19 23:19:44 UTC')
💡 saved: Run(uid='qr3Y8hbIkRy7F8npDdVq', transform_id=3, created_by_id=1)
Run(uid='qr3Y8hbIkRy7F8npDdVq', started_at='2024-06-19 23:19:44 UTC', is_consecutive=True, transform_id=3, created_by_id=1)

Inspect the CellMarker registry

Inspect your aggregated cell marker registry as a DataFrame:

bt.CellMarker.df().head()
uid name synonyms gene_symbol ncbi_gene_id uniprotkb_id organism_id public_source_id run_id created_by_id updated_at
id
41 7SyRazPQeCqG CD14/19 None None None None 1 NaN 2 1 2024-06-19 23:19:38.747604+00:00
40 6ASIQ7GR2c39 CD103 ITGAE 3682 P38570 1 26.0 2 1 2024-06-19 23:19:38.735623+00:00
39 7OES2NXy0W6C CD69 CD69 969 Q07108 1 26.0 2 1 2024-06-19 23:19:38.735517+00:00
38 4Y0JkNLWc8tl CD49B ITGA2 3673 P17301 1 26.0 2 1 2024-06-19 23:19:38.735406+00:00
37 2ddvD3rZZ38f CXCR4 CXCR4 7852 P61073 1 26.0 2 1 2024-06-19 23:19:38.735295+00:00

Search for a marker (synonyms aware):

bt.CellMarker.search("PD-1").df().head(2)
uid name synonyms gene_symbol ncbi_gene_id uniprotkb_id organism_id public_source_id run_id created_by_id updated_at
id
29 6c7MomnrsfYu PD1 PID1|PD-1|PD 1 PDCD1 5133 A0A0M3M0G7 1 26 1 1 2024-06-19 23:19:22.747891+00:00

Look up markers with auto-complete:

markers = bt.CellMarker.lookup()
markers.cd8
CellMarker(uid='5YxpB5QNiCWr', name='CD8', synonyms='', gene_symbol='CD8A', ncbi_gene_id='925', uniprotkb_id='P01732', created_by_id=1, run_id=1, organism_id=1, public_source_id=26, updated_at='2024-06-19 23:19:22 UTC')

Query artifacts by markers

Query panels and collections based on markers, e.g., which collections have 'CD8' in the flow panel:

panels_with_cd8 = ln.FeatureSet.filter(cell_markers=markers.cd8).all()
ln.Artifact.filter(feature_sets__in=panels_with_cd8).df()
uid version description key suffix type accessor size hash hash_type n_objects n_observations visibility key_is_virtual storage_id transform_id run_id created_by_id updated_at
id
1 txYXcllRpOqQznDQu1bz None Alpert19 None .h5ad dataset AnnData 33374864 QNP1c3p6scaAwPo9AW8fLw md5 None 166537 1 True 1 1 1 1 2024-06-19 23:19:27.980620+00:00
2 CLfzXf5jEJVr9FkMKkXG None Oetjen18_t1 None .h5ad dataset AnnData 46506448 WbPHGIMM_5GT68rC8ZydHA md5 None 241552 1 True 1 2 2 1 2024-06-19 23:19:39.310346+00:00

Access registries:

features = ln.Feature.lookup()

Find shared cell markers between two files:

artifacts = ln.Artifact.filter(feature_sets__in=panels_with_cd8).list()
shared_markers = artifacts[0].features["var"] & artifacts[1].features["var"]
shared_markers.list("name")
['Cd4', 'CD8', 'CD3', 'CD27', 'Ccr7', 'CD45RA']