facs3/4 Jupyter Notebook lamindata

Query & integrate data

import lamindb as ln
import bionty as bt

ln.track("wukchS8V976U0000")
→ connected lamindb: testuser1/test-facs
→ notebook imports: bionty==0.51.2 lamindb==0.76.12
→ created Transform('wukchS8V'), started new Run('GF38j6Th') at 2024-10-11 09:35:38 UTC

Inspect the CellMarker registry

Inspect your aggregated cell marker registry as a DataFrame:

bt.CellMarker.df().head()
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uid name synonyms description gene_symbol ncbi_gene_id uniprotkb_id source_id organism_id run_id created_at created_by_id
id
41 3ZFziy5ims8J CD14/19 None None None None None NaN 1 2 2024-10-11 09:35:32.542940+00:00 1
40 31nZfqQo8yZg CD103 None ITGAE 3682 P38570 28.0 1 2 2024-10-11 09:35:32.532038+00:00 1
39 1iLDs6cZIpxj CD69 None CD69 969 Q07108 28.0 1 2 2024-10-11 09:35:32.532006+00:00 1
38 525YfNUB967z CD49B None ITGA2 3673 P17301 28.0 1 2 2024-10-11 09:35:32.531974+00:00 1
37 3IPMBjs68Vy1 CXCR4 None CXCR4 7852 P61073 28.0 1 2 2024-10-11 09:35:32.531934+00:00 1

Search for a marker (synonyms aware):

bt.CellMarker.search("PD-1").df().head(2)
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uid name synonyms description gene_symbol ncbi_gene_id uniprotkb_id source_id organism_id run_id created_at created_by_id
id
29 33vFR1q26vnM PD1 PID1|PD-1|PD 1 None PDCD1 5133 A0A0M3M0G7 28 1 1 2024-10-11 09:35:15.731669+00:00 1

Look up markers with auto-complete:

markers = bt.CellMarker.lookup()
markers.cd8
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CellMarker(uid='1xRpnOHIkdyE', name='CD8', synonyms='', gene_symbol='CD8A', ncbi_gene_id='925', uniprotkb_id='P01732', created_by_id=1, run_id=1, source_id=28, organism_id=1, created_at=2024-10-11 09:35:15 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()
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uid version is_latest description key suffix type size hash n_objects n_observations _hash_type _accessor visibility _key_is_virtual storage_id transform_id run_id created_at created_by_id
id
1 t5XWF2JQFKnoAEJM0000 None True Alpert19 None .h5ad dataset 33374864 QNP1c3p6scaAwPo9AW8fLw None 166537 md5 AnnData 1 True 1 1 1 2024-10-11 09:35:19.327930+00:00 1
2 5OT94EFgfOISnNz00000 None True Oetjen18_t1 None .h5ad dataset 46506448 WbPHGIMM_5GT68rC8ZydHA None 241552 md5 AnnData 1 True 1 2 2 2024-10-11 09:35:32.984337+00:00 1

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")
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['Cd4', 'CD8', 'CD3', 'CD27', 'Ccr7', 'CD45RA']