Query & integrate data#
import lamindb as ln
import lnschema_bionty as lb
lb.settings.species = "human"
馃挕 loaded instance: testuser1/test-facs (lamindb 0.54.3)
ln.track()
馃挕 notebook imports: lamindb==0.54.3 lnschema_bionty==0.31.2
馃挕 Transform(id='wukchS8V976Uz8', name='Query & integrate data', short_name='facs2', version='0', type=notebook, updated_at=2023-09-29 14:47:53, created_by_id='DzTjkKse')
馃挕 Run(id='3HO1j5jrH4UITSiq2y4N', run_at=2023-09-29 14:47:53, transform_id='wukchS8V976Uz8', created_by_id='DzTjkKse')
Inspect the CellMarker registry 
#
Inspect your aggregated cell marker registry as a DataFrame:
lb.CellMarker.filter().df().head()
| name | synonyms | gene_symbol | ncbi_gene_id | uniprotkb_id | species_id | bionty_source_id | updated_at | created_by_id | |
|---|---|---|---|---|---|---|---|---|---|
| id | |||||||||
| hVNEgxlcDV10 | CD127 | IL7R | 3575 | P16871 | uHJU | qmjJ | 2023-09-29 14:47:34 | DzTjkKse | |
| lRZYuH929QDw | CD85j | None | None | None | uHJU | qmjJ | 2023-09-29 14:47:34 | DzTjkKse | |
| roEbL8zuLC5k | Cd14 | CD14 | 4695 | O43678 | uHJU | qmjJ | 2023-09-29 14:47:34 | DzTjkKse | |
| N2F6Qv9CxJch | CD11B | ITGAM | 3684 | P11215 | uHJU | qmjJ | 2023-09-29 14:47:34 | DzTjkKse | |
| a4hvNp34IYP0 | CD3 | None | None | None | uHJU | qmjJ | 2023-09-29 14:47:34 | DzTjkKse |
Search for a marker (synonyms aware):
lb.CellMarker.search("PD-1").head(2)
| id | synonyms | __ratio__ | |
|---|---|---|---|
| name | |||
| PD1 | 2VeZenLi2dj5 | PID1|PD-1|PD 1 | 100.0 |
| Cd14 | roEbL8zuLC5k | 50.0 |
Look up markers with auto-complete:
markers = lb.CellMarker.lookup()
markers.cd14
CellMarker(id='roEbL8zuLC5k', name='Cd14', synonyms='', gene_symbol='CD14', ncbi_gene_id='4695', uniprotkb_id='O43678', updated_at=2023-09-29 14:47:34, species_id='uHJU', bionty_source_id='qmjJ', created_by_id='DzTjkKse')
Query files by markers #
Query panels and datasets based on markers, e.g., which datasets have 'CD14' in the flow panel:
panels_with_cd14 = ln.FeatureSet.filter(cell_markers=markers.cd14).all()
ln.File.filter(feature_sets__in=panels_with_cd14).df()
| storage_id | key | suffix | accessor | description | version | size | hash | hash_type | transform_id | run_id | initial_version_id | updated_at | created_by_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||||
| WmQQuZQyOo5E4NkwzF1r | uyxQ7P2z | None | .h5ad | AnnData | Alpert19 | None | 33369696 | Piw2n0vdnoNoAV7ZxgsW-g | md5 | OWuTtS4SAponz8 | XrTJbAKJspEWgiAhQCHj | None | 2023-09-29 14:47:40 | DzTjkKse |
| 8SS4VRw5xd4ihh9lMn2o | uyxQ7P2z | None | .h5ad | AnnData | Flow cytometry file 2 | None | 6837528 | t6plg-pXZMxqmQN9naNeuw | md5 | SmQmhrhigFPLz8 | Eb4qSnYGiAUnPvezF0Ip | None | 2023-09-29 14:47:49 | DzTjkKse |
Access registries:
features = ln.Feature.lookup()
Find shared cell markers between two files:
files = ln.File.filter(feature_sets__in=panels_with_cd14).list()
file1, file2 = files[0], files[1]
shared_markers = file1.features["var"] & file2.features["var"]
shared_markers.list("name")
['CD127', 'Cd14', 'CD3', 'Ccr7', 'Cd19', 'CD28', 'CD27', 'Cd4', 'CD8', 'CD57']