scrna3/6 Jupyter Notebook lamindata

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 collection.

import lamindb as ln
import bionty as bt
💡 connected lamindb: testuser1/test-scrna
ln.settings.transform.stem_uid = "agayZTonayqA"
ln.settings.transform.version = "1"
ln.track()
💡 notebook imports: bionty==0.44.0 lamindb==0.74.0
💡 saved: Transform(uid='agayZTonayqA5zKv', version='1', name='Query artifacts', key='scrna3', type='notebook', created_by_id=1, updated_at='2024-06-19 23:18:24 UTC')
💡 saved: Run(uid='cgRNCDbxhPjPTL3Wi4kD', transform_id=3, created_by_id=1)
Run(uid='cgRNCDbxhPjPTL3Wi4kD', started_at='2024-06-19 23:18:24 UTC', is_consecutive=True, 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").df()
uid version name key description type reference reference_type latest_report_id source_code_id created_by_id updated_at
id
1 Nv48yAceNSh85zKv 1 scRNA-seq scrna None notebook None None None None 1 2024-06-19 23:15:24.846998+00:00
2 ManDYgmftZ8C5zKv 1 Standardize and append a batch of data scrna2 None notebook None None None None 1 2024-06-19 23:17:55.943031+00:00
3 agayZTonayqA5zKv 1 Query artifacts scrna3 None notebook None None None None 1 2024-06-19 23:18:24.171274+00:00
transform = ln.Transform.filter(uid="Nv48yAceNSh85zKv").one()
ln.Artifact.filter(transform=transform).df()
uid version description key suffix type accessor size hash hash_type n_objects n_observations visibility key_is_virtual storage_id transform_id run_id created_by_id updated_at
id
1 ewuV08zrWRrpUyJEjE6N None Human immune cells from Conde22 None .h5ad dataset AnnData 57612943 9sXda5E7BYiVoDOQkTC0KB sha1-fl None 1648 1 True 1 1 1 1 2024-06-19 23:17:50.237700+00:00

Query artifacts by biological metadata

organism = bt.Organism.lookup()
tissues = bt.Tissue.lookup()
query = ln.Artifact.filter(
    organisms=organism.human,
    tissues=tissues.bone_marrow,
)
query.df()
uid version description key suffix type accessor size hash hash_type n_objects n_observations visibility key_is_virtual storage_id transform_id run_id created_by_id updated_at
id

Inspect artifact metadata

query_set = ln.Artifact.filter().all()
artifact1, artifact2 = query_set[0], query_set[1]
artifact1.describe()
Artifact(uid='ewuV08zrWRrpUyJEjE6N', description='Human immune cells from Conde22', suffix='.h5ad', type='dataset', accessor='AnnData', size=57612943, hash='9sXda5E7BYiVoDOQkTC0KB', hash_type='sha1-fl', n_observations=1648, visibility=1, key_is_virtual=True, updated_at='2024-06-19 23:17:50 UTC')
  Provenance
    .created_by = 'testuser1'
    .storage = '/home/runner/work/lamin-usecases/lamin-usecases/docs/test-scrna'
    .transform = 'scRNA-seq'
    .run = '2024-06-19 23:15:24 UTC'
    .input_of = ["'2024-06-19 23:17:55 UTC'"]
  Labels
    .tissues = 'blood', 'thoracic lymph node', 'spleen', 'lung', 'mesenteric lymph node', 'lamina propria', 'liver', 'jejunal epithelium', 'omentum', 'bone marrow', ...
    .cell_types = '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 = '10x 3' v3', '10x 5' v2', '10x 5' v1'
    .ulabels = 'D496', '621B', 'A29', 'A36', 'A35', '637C', 'A52', 'A37', 'D503', '640C', ...
  Features
    'donor' = 'D496', '621B', 'A29', 'A36', 'A35', '637C', 'A52', 'A37', 'D503', '640C', ...
    'tissue' = 'blood', 'thoracic lymph node', 'spleen', 'lung', 'mesenteric lymph node', 'lamina propria', 'liver', 'jejunal epithelium', 'omentum', 'bone marrow', ...
    'cell_type' = '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' = '10x 3' v3', '10x 5' v2', '10x 5' v1'
  Feature sets
    'var' = 'MIR1302-2HG', 'FAM138A', 'OR4F5', 'None', 'OR4F29', 'OR4F16', 'LINC01409', 'FAM87B', 'LINC01128', 'LINC00115', 'FAM41C'
    'obs' = 'donor', 'tissue', 'cell_type', 'assay'
artifact1.view_lineage()
_images/3be1b52bfc44e17b0e6dae9a5bf7a99e9a9ed2996e9d9c9606b45df1c449d4b4.svg
artifact2.describe()
Artifact(uid='kSs9STRn8LxsjEBeSyAW', description='10x reference adata', suffix='.h5ad', type='dataset', accessor='AnnData', size=857752, hash='PnpU6XI5Fbzwc49XgrgdNg', hash_type='md5', n_observations=70, visibility=1, key_is_virtual=True, updated_at='2024-06-19 23:18:17 UTC')
  Provenance
    .created_by = 'testuser1'
    .storage = '/home/runner/work/lamin-usecases/lamin-usecases/docs/test-scrna'
    .transform = 'Standardize and append a batch of data'
    .run = '2024-06-19 23:17:55 UTC'
  Labels
    .cell_types = 'dendritic cell', 'CD4-positive helper T cell', 'effector memory CD4-positive, alpha-beta T cell, terminally differentiated', 'cytotoxic T cell', 'CD8-positive, CD25-positive, alpha-beta regulatory T cell', 'CD14-positive, CD16-positive monocyte', 'CD16-positive, CD56-dim natural killer cell, human', 'B cell, CD19-positive', 'CD38-positive naive B cell'
  Features
    'cell_type' = 'dendritic cell', 'CD4-positive helper T cell', 'effector memory CD4-positive, alpha-beta T cell, terminally differentiated', 'cytotoxic T cell', 'CD8-positive, CD25-positive, alpha-beta regulatory T cell', 'CD14-positive, CD16-positive monocyte', 'CD16-positive, CD56-dim natural killer cell, human', 'B cell, CD19-positive', 'CD38-positive naive B cell'
  Feature sets
    'var' = 'TLE5', 'S1PR4', 'CD164', 'SMIM24', 'DCAF10', 'RAB13', 'TPM3', 'HES4', 'HAX1', 'ADD3', 'GSTK1', 'GTF3C6', 'SNX2', 'ACAA1', 'MATK', 'ZYX', 'JAML', 'CD3E', 'CD3D', 'EXOG'
    'obs' = 'cell_type'
artifact2.view_lineage()
_images/b6fba4fdd0d2dee68a0779360b5df38791e3727082acfb900791f0986a519697.svg

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
['CD4-positive helper T cell',
 '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 collections 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)]