Query & integrate data#
import lamindb as ln
import bionty as bt
馃挕 connected lamindb: testuser1/test-facs
ln.settings.transform.stem_uid = "wukchS8V976U"
ln.settings.transform.version = "0"
ln.track()
馃挕 notebook imports: bionty==0.42.9 lamindb==0.70.4
馃挕 saved: Transform(uid='wukchS8V976U6K79', name='Query & integrate data', key='facs3', version='0', type='notebook', updated_at=2024-04-24 12:51:29 UTC, created_by_id=1)
馃挕 saved: Run(uid='5iFgIADSbhHg9QKYrBuR', 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 | created_at | updated_at | created_by_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| id | |||||||||||
| 41 | 7SyRazPQeCqG | CD14/19 | None | None | None | None | 1 | NaN | 2024-04-24 12:51:23.085137+00:00 | 2024-04-24 12:51:23.085173+00:00 | 1 |
| 40 | 6ASIQ7GR2c39 | CD103 | ITGAE | 3682 | P38570 | 1 | 18.0 | 2024-04-24 12:51:23.048816+00:00 | 2024-04-24 12:51:23.048827+00:00 | 1 | |
| 39 | 7OES2NXy0W6C | CD69 | CD69 | 969 | Q07108 | 1 | 18.0 | 2024-04-24 12:51:23.048722+00:00 | 2024-04-24 12:51:23.048733+00:00 | 1 | |
| 38 | 4Y0JkNLWc8tl | CD49B | ITGA2 | 3673 | P17301 | 1 | 18.0 | 2024-04-24 12:51:23.048628+00:00 | 2024-04-24 12:51:23.048638+00:00 | 1 | |
| 37 | 2ddvD3rZZ38f | CXCR4 | CXCR4 | 7852 | P61073 | 1 | 18.0 | 2024-04-24 12:51:23.048531+00:00 | 2024-04-24 12:51:23.048542+00:00 | 1 |
Search for a marker (synonyms aware):
bt.CellMarker.search("PD-1").head(2)
| uid | synonyms | score | |
|---|---|---|---|
| name | |||
| PD1 | 6c7MomnrsfYu | PID1|PD-1|PD 1 | 100.0 |
| CD14/19 | 7SyRazPQeCqG | 54.5 |
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', updated_at=2024-04-24 12:51:06 UTC, organism_id=1, public_source_id=18, created_by_id=1)
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 | 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 | |||||||||||||||||||
| 1 | QZJZ3w7AOKMSzjH6uokP | 1 | None | .h5ad | AnnData | Alpert19 | None | 33374864 | QNP1c3p6scaAwPo9AW8fLw | md5 | None | None | 1 | 1 | 1 | True | 2024-04-24 12:51:12.668479+00:00 | 2024-04-24 12:51:12.792439+00:00 | 1 |
| 2 | XeirTtqka4bFtILffK2J | 1 | None | .h5ad | AnnData | Oetjen18_t1 | None | 46506448 | WbPHGIMM_5GT68rC8ZydHA | md5 | None | None | 2 | 2 | 1 | True | 2024-04-24 12:51:23.501929+00:00 | 2024-04-24 12:51:23.582441+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()
file1, file2 = artifacts[0], artifacts[1]
shared_markers = file1.features["var"] & file2.features["var"]
shared_markers.list("name")
['Cd4', 'CD8', 'CD3', 'CD27', 'Ccr7', 'CD45RA']