Query & integrate data¶
import lamindb as ln
import bionty as bt
ln.track("wukchS8V976U0000")
→ connected lamindb: testuser1/test-facs
→ created Transform('wukchS8V976U0000'), started new Run('INu3VKyy...') at 2025-01-20 07:39:15 UTC
→ notebook imports: bionty==1.0.0 lamindb==1.0.2
Inspect the CellMarker registry 
¶
Inspect your aggregated cell marker registry as a DataFrame:
bt.CellMarker.df().head()
Show code cell output
| uid | name | synonyms | description | gene_symbol | ncbi_gene_id | uniprotkb_id | space_id | source_id | organism_id | run_id | created_at | created_by_id | _aux | _branch_code | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | |||||||||||||||
| 41 | 3ZFziy5ims8J | CD14/19 | None | None | None | None | None | 1 | NaN | 1 | 2 | 2025-01-20 07:39:09.161000+00:00 | 1 | None | 1 |
| 36 | 5c4A0r7gMiGw | CD95 | None | FAS | 2194 | P49327 | 1 | 28.0 | 1 | 2 | 2025-01-20 07:39:09.151000+00:00 | 1 | None | 1 | |
| 37 | 3IPMBjs68Vy1 | CXCR4 | None | CXCR4 | 7852 | P61073 | 1 | 28.0 | 1 | 2 | 2025-01-20 07:39:09.151000+00:00 | 1 | None | 1 | |
| 38 | 525YfNUB967z | CD49B | None | ITGA2 | 3673 | P17301 | 1 | 28.0 | 1 | 2 | 2025-01-20 07:39:09.151000+00:00 | 1 | None | 1 | |
| 39 | 1iLDs6cZIpxj | CD69 | None | CD69 | 969 | Q07108 | 1 | 28.0 | 1 | 2 | 2025-01-20 07:39:09.151000+00:00 | 1 | None | 1 |
Search for a marker (synonyms aware):
bt.CellMarker.search("PD-1").df().head(2)
Show code cell output
| uid | name | synonyms | description | gene_symbol | ncbi_gene_id | uniprotkb_id | space_id | source_id | organism_id | run_id | created_at | created_by_id | _aux | _branch_code | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | |||||||||||||||
| 29 | 33vFR1q26vnM | PD1 | PID1|PD-1|PD 1 | None | PDCD1 | 5133 | A0A0M3M0G7 | 1 | 28 | 1 | 1 | 2025-01-20 07:38:57.129000+00:00 | 1 | None | 1 |
Look up markers with auto-complete:
markers = bt.CellMarker.lookup()
markers.cd8
Show code cell output
CellMarker(uid='1xRpnOHIkdyE', name='CD8', synonyms='', gene_symbol='CD8A', ncbi_gene_id='925', uniprotkb_id='P01732', created_by_id=1, run_id=1, space_id=1, source_id=28, organism_id=1, created_at=2025-01-20 07:38:57 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()
Show code cell output
| uid | key | description | suffix | kind | otype | size | hash | n_files | n_observations | _hash_type | _key_is_virtual | _overwrite_versions | space_id | storage_id | schema_id | version | is_latest | run_id | created_at | created_by_id | _aux | _branch_code | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | |||||||||||||||||||||||
| 1 | 4q0QrlBS4kevoDq60000 | None | Alpert19 | .h5ad | dataset | AnnData | 33374864 | QNP1c3p6scaAwPo9AW8fLw | None | 166537 | md5 | True | False | 1 | 1 | None | None | True | 1 | 2025-01-20 07:39:00.005000+00:00 | 1 | None | 1 |
| 2 | 5s56cWrWhpiAwh5h0000 | None | Oetjen18_t1 | .h5ad | dataset | AnnData | 46506448 | WbPHGIMM_5GT68rC8ZydHA | None | 241552 | md5 | True | False | 1 | 1 | None | None | True | 2 | 2025-01-20 07:39:09.544000+00:00 | 1 | None | 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")
Show code cell output
['Cd4', 'CD8', 'CD3', 'CD27', 'Ccr7', 'CD45RA']