Query & integrate data#

import lamindb as ln
import lnschema_bionty as lb

lb.settings.species = "human"

💡 loaded instance: testuser1/test-facs (lamindb 0.54.2)

ln.track()

💡 notebook imports: lamindb==0.54.2 lnschema_bionty==0.31.2

💡 Transform(id='wukchS8V976Uz8', name='Query & integrate data', short_name='facs2', version='0', type=notebook, updated_at=2023-09-27 19:03:58, created_by_id='DzTjkKse')

💡 Run(id='eA3SzcdxdSmlKbTlpgUp', run_at=2023-09-27 19:03:58, 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
0qCmUijBeByY	CD94		KLRD1	3824	Q13241	uHJU	RlqM	2023-09-27 19:03:35	DzTjkKse
bspnQ0igku6c	CD16		FCGR3A	2215	O75015	uHJU	RlqM	2023-09-27 19:03:35	DzTjkKse
ttBc0Fs01sYk	CD8		CD8A	925	P01732	uHJU	RlqM	2023-09-27 19:03:35	DzTjkKse
ljp5UfCF9HCi	TCRgd	TCRGAMMADELTA\|TCRγδ	None	None	None	uHJU	RlqM	2023-09-27 19:03:35	DzTjkKse
yCyTIVxZkIUz	DNA2		DNA2	1763	P51530	uHJU	RlqM	2023-09-27 19:03:35	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
CD16	bspnQ0igku6c		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=2023-09-27 19:03:35, species_id='uHJU', bionty_source_id='RlqM', 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
RY2suClyjVaJf7WYC0SH	VvPocT7z	None	.h5ad	AnnData	Flow cytometry file 2	None	6837528	aWYCHE1-26gzAU6rlgoMtQ	md5	SmQmhrhigFPLz8	iKuM6oyOKGVBDvx0YQ7P	None	2023-09-27 19:03:52	DzTjkKse
8RZdIbll16NTrAwo7lRL	VvPocT7z	None	.h5ad	AnnData	See dataset 8RZdIbll16NTrAwo7lRL	None	33369696	fnzTGHE8BlkiMMIqHLDjyA	md5	OWuTtS4SAponz8	6LxzHJKBOJu5s56VPocZ	None	2023-09-27 19:03:42	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")

['CD27', 'CD8', 'Ccr7', 'CD57', 'Cd4', 'CD3', 'Cd19', 'CD28', 'CD127', 'Cd14']