Query artifacts

Here, we’ll query artifacts and inspect their metadata.

This guide can be skipped if you are only interested in how to leverage the overall dataset.

import lamindb as ln
import lnschema_bionty as lb
import anndata as ad

💡 lamindb instance: testuser1/test-scrna

ln.track()

💡 notebook imports: anndata==0.9.2 lamindb==0.64.2 lnschema_bionty==0.36.1

💡 saved: Transform(uid='agayZTonayqAz8', name='Query artifacts', short_name='scrna3', version='0', type=notebook, updated_at=2023-12-22 16:56:12 UTC, created_by_id=1)

💡 saved: Run(uid='aIIa6Ll02VsLGk2bJKD8', run_at=2023-12-22 16:56:12 UTC, transform_id=3, created_by_id=1)

Query artifacts by provenance metadata

users = ln.User.lookup()

ln.Transform.filter(created_by=users.testuser1).search("scrna")

	uid	score
name
scRNA-seq	Nv48yAceNSh8z8	90.0
Standardize and append a batch of data	ManDYgmftZ8Cz8	45.0
Query artifacts	agayZTonayqAz8	36.0

transform = ln.Transform.filter(uid="Nv48yAceNSh8z8").one()

ln.Artifact.filter(transform=transform).df()

	uid	storage_id	key	suffix	accessor	description	version	size	hash	hash_type	n_objects	n_observations	transform_id	run_id	initial_version_id	visibility	key_is_virtual	updated_at	created_by_id
id
1	Nnw68SLvhQgFMBAnMMCe	1	scrna/conde22.h5ad	.h5ad	AnnData	Human immune cells from Conde22	None	57612943	9sXda5E7BYiVoDOQkTC0KB	sha1-fl	None	None	1	1	None	1	True	2023-12-22 16:55:39.771165+00:00	1

Query artifacts by biological metadata

assays = lb.ExperimentalFactor.lookup()
organism = lb.Organism.lookup()
cell_types = lb.CellType.lookup()

query = ln.Artifact.filter(
    experimental_factors=assays.single_cell_rna_sequencing,
    organism=organism.human,
    cell_types=cell_types.gamma_delta_t_cell,
)

query.df()

	uid	storage_id	key	suffix	accessor	description	version	size	hash	hash_type	n_objects	n_observations	transform_id	run_id	initial_version_id	visibility	key_is_virtual	updated_at	created_by_id
id
1	Nnw68SLvhQgFMBAnMMCe	1	scrna/conde22.h5ad	.h5ad	AnnData	Human immune cells from Conde22	None	57612943	9sXda5E7BYiVoDOQkTC0KB	sha1-fl	None	None	1	1	None	1	True	2023-12-22 16:55:39.771165+00:00	1

Inspect artifact metadata

query_set = ln.Artifact.filter().all()

artifact1, artifact2 = query_set[0], query_set[1]

artifact1.describe()

Artifact(uid='Nnw68SLvhQgFMBAnMMCe', key='scrna/conde22.h5ad', suffix='.h5ad', accessor='AnnData', description='Human immune cells from Conde22', size=57612943, hash='9sXda5E7BYiVoDOQkTC0KB', hash_type='sha1-fl', visibility=1, key_is_virtual=True, updated_at=2023-12-22 16:55:39 UTC)

Provenance:
  🗃️ storage: Storage(uid='4Pr4dBVo', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/test-scrna', type='local', updated_at=2023-12-22 16:55:18 UTC, created_by_id=1)
  📔 transform: Transform(uid='Nv48yAceNSh8z8', name='scRNA-seq', short_name='scrna', version='0', type='notebook', updated_at=2023-12-22 16:55:22 UTC, created_by_id=1)
  👣 run: Run(uid='ElzFsV2oOOJ4AwZTIKXd', run_at=2023-12-22 16:55:22 UTC, transform_id=1, created_by_id=1)
  👤 created_by: User(uid='DzTjkKse', handle='testuser1', name='Test User1', updated_at=2023-12-22 16:55:18 UTC)
  ⬇️ input_of (core.Run): ['2023-12-22 16:55:45 UTC']
Features:
  var: FeatureSet(uid='3yO9udhgk40tOrZceGC9', n=36390, type='number', registry='bionty.Gene', hash='rMZltwoBCMdVPVR8x6nJ', updated_at=2023-12-22 16:55:36 UTC, created_by_id=1)
    'MIR1302-2HG', 'FAM138A', 'OR4F5', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'OR4F29', 'None', 'OR4F16', 'None', 'LINC01409', 'FAM87B', 'LINC01128', 'LINC00115', 'FAM41C', 'None', ...
  obs: FeatureSet(uid='0HngcQZopPzGbZL3d8pT', n=4, registry='core.Feature', hash='EXQTQYCYNl0l1_OLBFOU', updated_at=2023-12-22 16:55:37 UTC, created_by_id=1)
    🔗 cell_type (32, bionty.CellType): 'classical monocyte', 'T follicular helper cell', 'memory B cell', 'alveolar macrophage', 'naive thymus-derived CD4-positive, alpha-beta T cell', 'effector memory CD8-positive, alpha-beta T cell, terminally differentiated', 'alpha-beta T cell', 'CD4-positive helper T cell', 'naive thymus-derived CD8-positive, alpha-beta T cell', 'macrophage', ...
    🔗 assay (4, bionty.ExperimentalFactor): 'single-cell RNA sequencing', '10x 3' v3', '10x 5' v2', '10x 5' v1'
    🔗 tissue (17, bionty.Tissue): 'blood', 'thoracic lymph node', 'spleen', 'lung', 'mesenteric lymph node', 'lamina propria', 'liver', 'jejunal epithelium', 'omentum', 'bone marrow', ...
    🔗 donor (12, core.ULabel): 'D496', '621B', 'A29', 'A36', 'A35', '637C', 'A52', 'A37', 'D503', '640C', ...
Labels:
  🏷️ organism (1, bionty.Organism): 'human'
  🏷️ tissues (17, bionty.Tissue): 'blood', 'thoracic lymph node', 'spleen', 'lung', 'mesenteric lymph node', 'lamina propria', 'liver', 'jejunal epithelium', 'omentum', 'bone marrow', ...
  🏷️ cell_types (32, bionty.CellType): 'classical monocyte', 'T follicular helper cell', 'memory B cell', 'alveolar macrophage', 'naive thymus-derived CD4-positive, alpha-beta T cell', 'effector memory CD8-positive, alpha-beta T cell, terminally differentiated', 'alpha-beta T cell', 'CD4-positive helper T cell', 'naive thymus-derived CD8-positive, alpha-beta T cell', 'macrophage', ...
  🏷️ experimental_factors (4, bionty.ExperimentalFactor): 'single-cell RNA sequencing', '10x 3' v3', '10x 5' v2', '10x 5' v1'
  🏷️ ulabels (12, core.ULabel): 'D496', '621B', 'A29', 'A36', 'A35', '637C', 'A52', 'A37', 'D503', '640C', ...

artifact1.view_flow()

artifact2.describe()

Artifact(uid='gUIlv4dU4IglKdM4cdct', suffix='.h5ad', accessor='AnnData', description='10x reference adata', size=853388, hash='eKH1ljAEh7Kd81-o2H4A7w', hash_type='md5', visibility=1, key_is_virtual=True, updated_at=2023-12-22 16:56:05 UTC)

Provenance:
  🗃️ storage: Storage(uid='4Pr4dBVo', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/test-scrna', type='local', updated_at=2023-12-22 16:55:18 UTC, created_by_id=1)
  📔 transform: Transform(uid='ManDYgmftZ8Cz8', name='Standardize and append a batch of data', short_name='scrna2', version='0', type='notebook', updated_at=2023-12-22 16:55:45 UTC, created_by_id=1)
  👣 run: Run(uid='O9AcqorQzSOcWWCfDof4', run_at=2023-12-22 16:55:45 UTC, transform_id=2, created_by_id=1)
  👤 created_by: User(uid='DzTjkKse', handle='testuser1', name='Test User1', updated_at=2023-12-22 16:55:18 UTC)
Features:
  var: FeatureSet(uid='nMX2IMfUs8TU1n59Wyhf', n=749, type='number', registry='bionty.Gene', hash='ZL6ScVsUK3gvyaiIVPVr', updated_at=2023-12-22 16:56:05 UTC, created_by_id=1)
    'IL18', 'NPM3', 'S100A9', 'S100A8', 'CNN2', 'ARHGAP45', 'RNF34', 'GPX4', 'S100A6', 'ADISSP', 'S100A4', 'FAM174C', 'SIT1', 'CCDC107', 'RSL1D1', 'TLN1', 'HES4', 'TNFRSF17', 'PCNA', 'RAB13', ...
  obs: FeatureSet(uid='OnPEcfePDEXo23pTgV2F', n=1, registry='core.Feature', hash='bqSXai02lq2zMp7z8MHE', updated_at=2023-12-22 16:56:05 UTC, created_by_id=1)
    🔗 cell_type (9, bionty.CellType): 'dendritic cell', 'CD38-positive naive B cell', 'B cell, CD19-positive', 'effector memory CD4-positive, alpha-beta T cell, terminally differentiated', 'CD16-positive, CD56-dim natural killer cell, human', 'CD4-positive, alpha-beta T cell', 'cytotoxic T cell', 'CD8-positive, CD25-positive, alpha-beta regulatory T cell', 'CD14-positive, CD16-negative classical monocyte'
  external: FeatureSet(uid='5VXX6OsehOQYUZFMvghG', n=2, registry='core.Feature', hash='07M1fkPNRSJ-HVFvwtX6', updated_at=2023-12-22 16:56:05 UTC, created_by_id=1)
    🔗 assay (1, bionty.ExperimentalFactor): 'single-cell RNA sequencing'
    🔗 organism (1, bionty.Organism): 'human'
Labels:
  🏷️ organism (1, bionty.Organism): 'human'
  🏷️ cell_types (9, bionty.CellType): 'dendritic cell', 'CD38-positive naive B cell', 'B cell, CD19-positive', 'effector memory CD4-positive, alpha-beta T cell, terminally differentiated', 'CD16-positive, CD56-dim natural killer cell, human', 'CD4-positive, alpha-beta T cell', 'cytotoxic T cell', 'CD8-positive, CD25-positive, alpha-beta regulatory T cell', 'CD14-positive, CD16-negative classical monocyte'
  🏷️ experimental_factors (1, bionty.ExperimentalFactor): 'single-cell RNA sequencing'

artifact2.view_flow()

Compare features

Here we compute shared genes:

artifact1_genes = artifact1.features["var"]
artifact2_genes = artifact2.features["var"]

shared_genes = artifact1_genes & artifact2_genes
len(shared_genes)

749

shared_genes.list("symbol")[:10]

['HES4',
 'TNFRSF4',
 'SSU72',
 'PARK7',
 'RBP7',
 'SRM',
 'MAD2L2',
 'AGTRAP',
 'TNFRSF1B',
 'EFHD2']

Compare cell types

artifact1_celltypes = artifact1.cell_types.all()
artifact2_celltypes = artifact2.cell_types.all()

shared_celltypes = artifact1_celltypes & artifact2_celltypes
shared_celltypes_names = shared_celltypes.list("name")
shared_celltypes_names

['CD16-positive, CD56-dim natural killer cell, human']

Load the individual artifacts

We could either load the artifacts into memory or access them in backed mode through .backed() to lazily load their content.

Let’s load them into memory:

adata1 = artifact1.load()
adata2 = artifact2.load()

We can now subset the two datasets by shared cell types:

adata1_subset = adata1[adata1.obs["cell_type"].isin(shared_celltypes_names)]
adata2_subset = adata2[adata2.obs["cell_type"].isin(shared_celltypes_names)]