Census release 2024-07-01 (LTS)¶
import lamindb as ln
import bionty as bt
from cellxgene_lamin import get_datasets_from_cxg, get_collections_from_cxg
ln.settings.verbosity = "hint"
💡 connected lamindb: laminlabs/cellxgene
census_version = "2024-07-01"
previous_release = "2023-12-15"
s3path = f"s3://cellxgene-data-public/cell-census/{census_version}/h5ads"
ln.UPath(s3path)
S3Path('s3://cellxgene-data-public/cell-census/2024-07-01/h5ads')
ln.UPath(s3path).view_tree()
0 sub-directories & 812 files with suffixes '.h5ad'
s3://cellxgene-data-public/cell-census/2024-07-01/h5ads
├── 0041b9c3-6a49-4bf7-8514-9bc7190067a7.h5ad
├── 00476f9f-ebc1-4b72-b541-32f912ce36ea.h5ad
├── 00e5dedd-b9b7-43be-8c28-b0e5c6414a62.h5ad
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├── 015c230d-650c-4527-870d-8a805849a382.h5ad
├── 019c7af2-c827-4454-9970-44d5e39ce068.h5ad
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├── 02792605-4760-4023-82ad-40fc4458a5db.h5ad
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├── 0fb7916e-7a68-4a4c-a441-3ab3989f29a7.h5ad
├── 0fdb6122-4600-40f0-a703-2da47cc7080d.h5ad
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├── 12967895-3d58-4e93-be2c-4e1bcf4388d5.h5ad
├── 1304e107-0f06-4d33-b634-d95ed986d02b.h5ad
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├── 182f6a56-7360-4924-a74e-1772e07b3031.h5ad
├── 18500fcd-9960-49cb-8a8e-7d868dc14efe.h5ad
├── 1873a18a-66fd-4a4d-8277-a872c93f5b59.h5ad
├── 18e2a8c5-33f7-455e-a58a-b2ba6921db27.h5ad
├── 18eb630b-a754-4111-8cd4-c24ec80aa5ec.h5ad
├── 19b21f40-db42-4a71-a0d6-913e83b17784.h5ad
├── 19e46756-9100-4e01-8b0e-23b557558a4c.h5ad
├── 1a018108-b4b6-457b-ba15-046d5e98c169.h5ad
├── 1a0610d8-1339-479b-b261-7fb586c3dab9.h5ad
├── 1a2e3350-28a8-4f49-b33c-5b67ceb001f6.h5ad
├── 1a38e762-2465-418f-b81c-6a4bce261c34.h5ad
├── 1a434263-2aa5-4fd9-a1f3-1c4a2a8fb771.h5ad
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├── 1b9d8702-5af8-4142-85ed-020eb06ec4f6.h5ad
├── 1c05b7ae-4dfb-4c1b-a441-4b23c04b169e.h5ad
...
ln.context.uid = "MIbO3QeJERcy0000"
ln.context.track()
💡 notebook imports: bionty==0.43.1 cellxgene_lamin==0.0.1 lamindb==0.74.1
💡 loaded: Transform(uid='MIbO3QeJERcy5zKv', version='1', name='Census release 2024-07-01 (LTS)', key='cencus-release-2024-07-01-LTS', type='notebook', created_by_id=1, updated_at='2024-07-16 12:49:15 UTC')
💡 loaded: Run(uid='1QV6wwhlHfLi2vMjjfOg', started_at='2024-07-16 12:49:41 UTC', finished_at='2024-07-16 12:26:21 UTC', is_consecutive=False, transform_id=22, created_by_id=1, environment_id=3663)
💡 tracked pip freeze > /Users/sunnysun/Library/Caches/lamindb/run_env_pip_1QV6wwhlHfLi2vMjjfOg.txt
Run(uid='1QV6wwhlHfLi2vMjjfOg', started_at='2024-07-16 12:49:41 UTC', finished_at='2024-07-16 12:26:21 UTC', is_consecutive=True, transform_id=22, created_by_id=1, environment_id=3663)
Get all datasets and associated metadata using cellxgene REST API:
cxg_datasets = get_datasets_from_cxg()
len(cxg_datasets)
1387
cxg_datasets[0].keys()
dict_keys(['assay', 'assets', 'cell_count', 'cell_type', 'citation', 'collection_doi', 'collection_doi_label', 'collection_id', 'collection_name', 'collection_version_id', 'dataset_id', 'dataset_version_id', 'development_stage', 'disease', 'donor_id', 'explorer_url', 'is_primary_data', 'mean_genes_per_cell', 'organism', 'primary_cell_count', 'processing_status', 'published_at', 'revised_at', 'revision_of_collection', 'revision_of_dataset', 'schema_version', 'self_reported_ethnicity', 'sex', 'spatial', 'suspension_type', 'tissue', 'title', 'tombstone', 'visibility', 'x_approximate_distribution'])
Register artifacts¶
artifacts = ln.Artifact.from_dir(s3path)
ln.save(artifacts)
❗ this creates one artifact per file in the directory - you might simply call ln.Artifact(dir) to get one artifact for the entire directory
✅ created 812 artifacts from directory using storage s3://cellxgene-data-public and key = cell-census/2024-07-01/h5ads/
artifacts = ln.Artifact.filter(key__contains=census_version).all()
len(artifacts)
812
artifacts.update(version=census_version)
812
for cxg_dataset in cxg_datasets:
artifact = artifacts.filter(key__contains=cxg_dataset["dataset_id"]).one_or_none()
if artifact is not None:
artifact.n_observations = cxg_dataset["cell_count"]
artifact.description = cxg_dataset["title"]
artifact.save()
artifacts_previous = ln.Artifact.filter(version=previous_release).all()
artifacts_previous.count()
1113
for artifact in artifacts:
dataset_id = artifact.key.split("/")[-1]
artifact_previous = artifacts_previous.filter(
key__endswith=dataset_id
).one_or_none()
if artifact_previous is not None:
artifact.add_to_version_family(artifact_previous, version=census_version)
Show code cell output
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Register metadata¶
Register new features and parent labels¶
artifacts = ln.Artifact.filter(key__contains=census_version).all()
from cellxgene_lamin._features import OBS_FEATURES, register_obs_featureset
if not artifacts[0].feature_sets.filter(name="obs metadata").exists():
obs_feature_set = register_obs_featureset(artifacts)
features = ln.Feature.lookup()
organisms¶
from cellxgene_lamin._organism import register_organisms, annotate_organisms
register_organisms(cxg_datasets)
Link collections and organisms to artifacts:
annotate_organisms(artifacts, cxg_datasets)
ontologies¶
Register all ontology ids:
from cellxgene_lamin._ontology import register_ontology_ids
register_ontology_ids(cxg_datasets)
Show code cell output
registering assay
✅ loaded 36 ExperimentalFactor records matching ontology_id: 'EFO:0700016', 'EFO:0008722', 'EFO:0010961', 'EFO:0030003', 'EFO:0008930', 'EFO:0008992', 'EFO:0030019', 'EFO:0030027', 'EFO:0700003', 'EFO:0030002', 'EFO:0008931', 'EFO:0008720', 'EFO:0009899', 'EFO:0009901', 'EFO:0010891', 'EFO:0008796', 'EFO:0008919', 'EFO:0011025', 'EFO:0009919', 'EFO:0700010', ...
✅ created 2 ExperimentalFactor records from Bionty matching ontology_id: 'EFO:0022490', 'EFO:0022492'
❗ now recursing through parents: this only happens once, but is much slower than bulk saving
💡 you can switch this off via: bt.settings.auto_save_parents = False
💡 also saving parents of ExperimentalFactor(uid='NuGX0K6A', name='ScaleBio single cell RNA sequencing', ontology_id='EFO:0022490', description='Scalebio Technology That Utilizes A Plate-Based Approach With Combinatorial Indexing To Generate Single Cell Libraries.', created_by_id=1, run_id=27, public_source_id=68, updated_at='2024-07-12 14:20:41 UTC')
💡 also saving parents of ExperimentalFactor(uid='5rfEtsEk', name='snm3C-seq', ontology_id='EFO:0022492', synonyms='sn-m3C-seq', description='A Method To Jointly Profile Chromatin Conformation And Cytosine Dna Methylation From The Same Cell.', molecule='DNA assay', instrument='assay by high throughput sequencer', created_by_id=1, run_id=27, public_source_id=68, updated_at='2024-07-12 14:20:41 UTC')
registering cell_type
✅ loaded 881 CellType records matching ontology_id: 'CL:4023036', 'CL:0003001', 'CL:4023041', 'CL:0000050', 'CL:0011024', 'CL:1001131', 'CL:0000695', 'CL:3000001', 'CL:2000097', 'CL:0000515', 'CL:0002328', 'CL:0000895', 'CL:0009010', 'CL:1001567', 'CL:0000935', 'CL:0000514', 'CL:0008031', 'CL:0000928', 'CL:1000353', 'CL:0000322', ...
✅ created 10 CellType records from Bionty matching ontology_id: 'CL:4030059', 'CL:4030061', 'CL:4030062', 'CL:4030063', 'CL:4030065', 'CL:4030066', 'CL:4030067', 'CL:4033056', 'CL:4033057', 'CL:4033058'
❗ now recursing through parents: this only happens once, but is much slower than bulk saving
💡 you can switch this off via: bt.settings.auto_save_parents = False
💡 also saving parents of CellType(uid='42PLafOv', name='L2/3 intratelencephalic projecting glutamatergic neuron', ontology_id='CL:4030059', description='An Intratelencephalic-Projecting Glutamatergic Neuron With A Soma Found In Cortical Layer 2/3.', created_by_id=1, run_id=27, public_source_id=60, updated_at='2024-07-12 14:20:43 UTC')
💡 also saving parents of CellType(uid='aECMKjUW', name='L3 intratelencephalic projecting glutamatergic neuron', ontology_id='CL:4030061', description='An Intratelencephalic-Projecting Glutamatergic Neuron With A Soma Found In Cortical Layer 3.', created_by_id=1, run_id=27, public_source_id=60, updated_at='2024-07-12 14:20:43 UTC')
💡 also saving parents of CellType(uid='6IlUXXLR', name='L4/5 intratelencephalic projecting glutamatergic neuron', ontology_id='CL:4030062', description='An Intratelencephalic-Projecting Glutamatergic With A Soma Located In Cortical Layer 4/5.', created_by_id=1, run_id=27, public_source_id=60, updated_at='2024-07-12 14:20:43 UTC')
💡 also saving parents of CellType(uid='2HrbvQGK', name='L4 intratelencephalic projecting glutamatergic neuron', ontology_id='CL:4030063', description='An Intratelencephalic-Projecting Glutamatergic Neuron With A Soma Found In Cortical Layer 4.', created_by_id=1, run_id=27, public_source_id=60, updated_at='2024-07-12 14:20:43 UTC')
💡 also saving parents of CellType(uid='2ZbG4sPC', name='L6 intratelencephalic projecting glutamatergic neuron', ontology_id='CL:4030065', description='An Intratelencephalic-Projecting Glutamatergic Neuron With A Soma Found In Cortical Layer 6.', created_by_id=1, run_id=27, public_source_id=60, updated_at='2024-07-12 14:20:43 UTC')
💡 also saving parents of CellType(uid='hRZYk1yi', name='ureteric bud cell', ontology_id='CL:4030066', description='An Epithelial Cell That Is Part Of A Ureteric Bud. A Ureteric Bud Cell Has The Potential To Induce Metanephric Mesenchymal Cells To Proliferate And Convert To Epithelia That Form Renal Tubules Via: (1) The Secretion Of Multiple Diffusible Growth Factors That Rescue Renal Progenitors From Apoptosis And Stimulate Them To Proliferate And (2) Contact-Dependent Mechanisms That Induce Mesenchymal-Epithelial Conversion.', created_by_id=1, run_id=27, public_source_id=60, updated_at='2024-07-12 14:20:43 UTC')
💡 also saving parents of CellType(uid='4wlSFvBI', name='L5/6 near-projecting glutamatergic neuron', ontology_id='CL:4030067', description='A Near-Projecting Glutamatergic Neuron With A Soma Found In Cortical Layer 5/6.', created_by_id=1, run_id=27, public_source_id=60, updated_at='2024-07-12 14:20:43 UTC')
💡 also saving parents of CellType(uid='5fugWvKl', name='onychocyte', ontology_id='CL:4033056', description='A Differentiated Flat Keratinocyte That Is Part Of A Nail Plate. An Onychocyte Is Firmly Adherent And Does Not Desquamate.', created_by_id=1, run_id=27, public_source_id=60, updated_at='2024-07-12 14:20:43 UTC')
💡 also saving parents of CellType(uid='2wh8CpfX', name='luminal adaptive secretory precursor cell of mammary gland', ontology_id='CL:4033057', synonyms='ER-negative luminal alveolar progenitor', description='A Luminal Epithelial Cell Of The Mammary Gland That Can Proliferate And Has The Potential To Differentiate Into A Lactocyte During Pregnancy. In Humans, A Luminal Adaptive Secretory Precursor Cell Can Be Identified By High Levels Of The Markers Epcam And Cd49F, And In Mice It Can Be Identified By Low Levels Of Cd29 And High Levels Of Cd14, Kit, Cd61, And Tspan8.', created_by_id=1, run_id=27, public_source_id=60, updated_at='2024-07-12 14:20:43 UTC')
💡 also saving parents of CellType(uid='1RLBZZWy', name='luminal hormone-sensing cell of mammary gland', ontology_id='CL:4033058', synonyms='ER-positive luminal hormone-sensor cell', description='A Luminal Epithelial Cell Of The Mammary Gland That Transduces Endocrine Cues To Orchestrate Proliferation, Architectural Remodeling, And Differentiation Of Other Cells In The Mammary Gland Via Paracrine Signaling. This Cell Expresses High Levels Of Estrogen Receptors. In Humans, A Luminal Hormone-Sensing Cell Can Be Identified By High Levels Of Epcam And Low Levels Of Cd49F, And In Mice It Can Be Identified By Low Levels Of Cd29 And High Levels Of Foxa1, Cd133, And Sca1 (Ly6A).', created_by_id=1, run_id=27, public_source_id=60, updated_at='2024-07-12 14:20:43 UTC')
✅ loaded 2 CellType records matching ontology_id: 'CL:0002326', 'CL:0000152'
✅ created 1 CellType record from Bionty matching ontology_id: 'CL:0000512'
💡 also saving parents of CellType(uid='6eUNBkGI', name='paracrine cell', ontology_id='CL:0000512', description='A Paracrine Cell Is A Secretory Cell That Secretes An Agonist Into Intercellular Spaces In Which It Diffuses To A Target Cell Other Than That Which Produced It.', created_by_id=1, run_id=27, public_source_id=60, updated_at='2024-07-12 14:20:47 UTC')
registering development_stage
✅ loaded 243 DevelopmentalStage records matching ontology_id: 'HsapDv:0000143', 'HsapDv:0000021', 'HsapDv:0000208', 'HsapDv:0000029', 'HsapDv:0000212', 'UBERON:0000113', 'HsapDv:0000030', 'MmusDv:0000054', 'HsapDv:0000166', 'HsapDv:0000058', 'HsapDv:0000107', 'HsapDv:0000120', 'MmusDv:0000046', 'HsapDv:0000198', 'MmusDv:0000072', 'MmusDv:0000091', 'HsapDv:0000057', 'HsapDv:0000140', 'HsapDv:0000096', 'HsapDv:0000256', ...
❗ did not create DevelopmentalStage record for 1 non-validated ontology_id: 'MmusDv:0000041'
✅ created 1 DevelopmentalStage record from Bionty matching ontology_id: 'MmusDv:0000041'
registered 1 records: [DevelopmentalStage(uid='6jNHUvWd', name='unknown', ontology_id='MmusDv:0000041', created_by_id=1, run_id=27, public_source_id=44)]
❗ now recursing through parents: this only happens once, but is much slower than bulk saving
💡 you can switch this off via: bt.settings.auto_save_parents = False
💡 also saving parents of DevelopmentalStage(uid='6jNHUvWd', name='unknown', ontology_id='MmusDv:0000041', created_by_id=1, run_id=27, public_source_id=44, updated_at='2024-07-12 14:20:50 UTC')
registering disease
✅ loaded 122 Disease records matching ontology_id: 'MONDO:0005147', 'MONDO:0005021', 'MONDO:0004849', 'MONDO:0005252', 'MONDO:0008608', 'MONDO:0005148', 'MONDO:0005016', 'MONDO:0002771', 'MONDO:0021178', 'MONDO:0002052', 'MONDO:0019622', 'MONDO:0001933', 'MONDO:0024660', 'MONDO:0005575', 'MONDO:0004994', 'MONDO:0021116', 'MONDO:0600025', 'MONDO:0002508', 'MONDO:0005061', 'MONDO:0005453', ...
❗ did not create Disease record for 1 non-validated ontology_id: 'MONDO:0100542'
registered 1 records: [Disease(uid='1sfLCprL', name='clonal hematopoiesis', ontology_id='MONDO:0100542', created_by_id=1, run_id=27)]
registering self_reported_ethnicity
registering sex
registering tissue
✅ loaded 417 Tissue records matching ontology_id: 'UBERON:0001836', 'UBERON:0000992', 'UBERON:0003532', 'UBERON:0000411', 'UBERON:8480030', 'UBERON:0039167', 'CL:0002328', 'UBERON:0003876', 'UBERON:0002119', 'UBERON:0007628', 'UBERON:0000955', 'UBERON:0000310', 'UBERON:0002382', 'UBERON:0002046', 'UBERON:0000059', 'UBERON:0034891', 'UBERON:0007224', 'UBERON:0000970', 'UBERON:0001049', 'UBERON:0000948', ...
❗ did not create Tissue record for 1 non-validated ontology_id: 'CL:0000307'
registered 1 records: [Tissue(uid='3bgY2Yz8', name='tracheal epithelial cell', ontology_id='CL:0000307', created_by_id=1, run_id=27)]
registering organism
# clean up the celltype in Tissue
import bionty as bt
bt.Tissue.filter(ontology_id='CL:0000307').delete()
bt.CellType.from_public(ontology_id='CL:0000307').save()
donors and suspension_types¶
from cellxgene_lamin._labels import register_ulabels
ln.settings.creation.search_names = False
register_ulabels(cxg_datasets, "donor_id")
ln.settings.creation.search_names = True
registered 1207 donor_ids
register_ulabels(cxg_datasets, "suspension_type")
registered 0 suspension_types
Annotate artifacts with obs metadata¶
from cellxgene_lamin._features import FEATURE_TO_ACCESSOR
import bionty as bt
features = ln.Feature.lookup()
for idx, cxg_dataset in enumerate(cxg_datasets):
if idx % 100 == 0:
print(f"annotating dataset {idx} of {len(cxg_datasets)}")
artifact = artifacts.filter(key__contains=cxg_dataset["dataset_id"]).one_or_none()
if artifact is None:
continue
for field, terms in cxg_dataset.items():
if field not in FEATURE_TO_ACCESSOR:
continue
accessor, orm = FEATURE_TO_ACCESSOR.get(field)
if field in ["donor_id", "suspension_type", "tissue_type"]:
records = orm.from_values(terms, field="name")
if len(records) > 0:
# stratify by feature so that link tables records are written
artifact.labels.add(records, feature=getattr(features, field))
else:
if field == "tissue":
records = []
celltypes = [i["ontology_term_id"] for i in terms if i["tissue_type"]=="cell culture"]
if len(celltypes) > 0:
# records += bt.CellType.from_values(
# [i["ontology_term_id"] for i in terms], field="ontology_id"
# )
print(f"These tissues are not yet linked: {celltypes}")
tissues = [i["ontology_term_id"] for i in terms if i["tissue_type"]!="cell culture"]
if len(tissues) > 0:
records += bt.Tissue.from_values(
[i["ontology_term_id"] for i in terms], field="ontology_id"
)
else:
records = orm.from_values(
[i["ontology_term_id"] for i in terms], field="ontology_id"
)
if len(records) > 0:
getattr(artifact, accessor).add(*records)
# clean up the 2 "unknowns" in DevelopmentalStage
bt.DevelopmentalStage.filter(name="unknown").exclude(ontology_id="unknown").delete()
annotating dataset 1370 of 1387
annotating dataset 1371 of 1387
annotating dataset 1372 of 1387
annotating dataset 1373 of 1387
annotating dataset 1374 of 1387
annotating dataset 1375 of 1387
annotating dataset 1376 of 1387
annotating dataset 1377 of 1387
annotating dataset 1378 of 1387
These tissues are not yet linked: ['CL:0002322']
annotating dataset 1379 of 1387
annotating dataset 1380 of 1387
annotating dataset 1381 of 1387
annotating dataset 1382 of 1387
annotating dataset 1383 of 1387
annotating dataset 1384 of 1387
annotating dataset 1385 of 1387
annotating dataset 1386 of 1387
Validate and register genes¶
from cellxgene_lamin._gene import register_genes
Register all genes for each organism:
register_genes()
registering homo_sapiens genes
✅ 62754 terms (100.00%) are validated for ensembl_gene_id
registering mus_musculus genes
✅ loaded 55281 Gene records matching ensembl_gene_id: 'ENSMUSG00000102693', 'ENSMUSG00000064842', 'ENSMUSG00000051951', 'ENSMUSG00000102851', 'ENSMUSG00000103377', 'ENSMUSG00000104017', 'ENSMUSG00000103025', 'ENSMUSG00000089699', 'ENSMUSG00000103201', 'ENSMUSG00000103147', 'ENSMUSG00000103161', 'ENSMUSG00000102331', 'ENSMUSG00000102348', 'ENSMUSG00000102592', 'ENSMUSG00000088333', 'ENSMUSG00000102343', 'ENSMUSG00000025900', 'ENSMUSG00000102948', 'ENSMUSG00000104123', 'ENSMUSG00000025902', ...
✅ created 1660 Gene records from Bionty matching ensembl_gene_id: 'ENSMUSG00000119939', 'ENSMUSG00000119940', 'ENSMUSG00000119941', 'ENSMUSG00000119942', 'ENSMUSG00000119943', 'ENSMUSG00000119944', 'ENSMUSG00000119945', 'ENSMUSG00000119946', 'ENSMUSG00000119947', 'ENSMUSG00000119948', 'ENSMUSG00000119949', 'ENSMUSG00000119950', 'ENSMUSG00000119951', 'ENSMUSG00000119952', 'ENSMUSG00000119953', 'ENSMUSG00000119954', 'ENSMUSG00000119955', 'ENSMUSG00000119956', 'ENSMUSG00000119957', 'ENSMUSG00000119958', ...
✅ 56941 terms (100.00%) are validated for ensembl_gene_id
registering synthetic_construct genes
✅ 92 terms (100.00%) are validated for ensembl_gene_id
registering severe_acute_respiratory_syndrome_coronavirus_2 genes
✅ 12 terms (100.00%) are validated for ensembl_gene_id
Link metadata to individual artifacts¶
annotate with genes measured in each artifact:
organisms = bt.Organism.lookup(field=bt.Organism.scientific_name)
for idx, artifact in enumerate(artifacts):
if idx % 100 == 0:
print(f"annotating dataset {idx} of {len(artifacts)}")
adata_backed = artifact.backed()
var_names = adata_backed.var_names
organism_record = artifact.organisms.first()
if organism_record is None:
print(f"No organism found for artifact: {artifact}")
continue
genes = bt.Gene.from_values(
var_names, field=bt.Gene.ensembl_gene_id, organism=organism_record
)
if len(genes) == 0 and var_names[0].startswith("ENSG"):
genes += bt.Gene.from_values(
var_names, field=bt.Gene.ensembl_gene_id, organism="human"
)
if len(var_names[var_names.str.startswith("ERCC")]) > 0:
genes += bt.Gene.from_values(
var_names,
field=bt.Gene.ensembl_gene_id,
organism=organisms.synthetic_construct,
)
if len(var_names[var_names.str.startswith("ENSSASG")]) > 0:
genes += bt.Gene.from_values(
var_names,
field=bt.Gene.ensembl_gene_id,
organism=organisms.severe_acute_respiratory_syndrome_coronavirus_2,
)
var_feature_set_artifact = ln.FeatureSet(genes, dtype="number")
var_feature_set_artifact.save()
artifact.feature_sets.add(
var_feature_set_artifact, through_defaults={"slot": "var"}
)
Show code cell output
annotating dataset 0 of 812
annotating dataset 100 of 812
annotating dataset 200 of 812
annotating dataset 300 of 812
annotating dataset 500 of 812
annotating dataset 600 of 812
annotating dataset 700 of 812
annotating dataset 800 of 812
artifact.describe()
Artifact(uid='g0RcSSYe5vQKzSWYkhMc', version='2024-07-01', description='Dissection: Amygdaloid complex (AMY) - basolateral nuclear group (BLN) - basolateral nucleus (basal nucleus) - BL', key='cell-census/2024-07-01/h5ads/fe1a73ab-a203-45fd-84e9-0f7fd19efcbd.h5ad', suffix='.h5ad', type='dataset', accessor='AnnData', size=391552151, hash='1V_lPFFOF51ioRTSVWx9Mg-47', hash_type='md5-n', n_observations=35285, visibility=1, key_is_virtual=False, updated_at='2024-07-12 12:40:48 UTC')
Provenance
.created_by = 'sunnyosun'
.storage = 's3://cellxgene-data-public'
.transform = 'Census release 2024-07-01 (LTS)'
.run = '2024-07-12 12:17:31 UTC'
Labels
.organisms = 'human'
.tissues = 'cerebral nuclei'
.cell_types = 'astrocyte', 'central nervous system macrophage', 'leukocyte', 'vascular associated smooth muscle cell', 'endothelial cell', 'ependymal cell', 'neuron', 'pericyte', 'fibroblast', 'oligodendrocyte precursor cell', ...
.diseases = 'normal'
.phenotypes = 'male'
.experimental_factors = '10x 3' v3'
.developmental_stages = '29-year-old human stage', '42-year-old human stage', '50-year-old human stage'
.ethnicities = 'European'
.ulabels = 'H18.30.002', 'H19.30.002', 'H19.30.001', 'nucleus'
Features
'donor_id' = 'H18.30.002', 'H19.30.001', 'H19.30.002'
'suspension_type' = 'nucleus'
'organism' = 'human'
Feature sets
'obs' = 'assay', 'cell_type', 'development_stage', 'disease', 'donor_id', 'self_reported_ethnicity', 'sex', 'tissue', 'organism', 'tissue_type', 'suspension_type'
'var' = 'None', 'EBF1', 'LINC02202', 'RNF145', 'LINC01932', 'UBLCP1', 'IL12B', 'RNU4ATAC2P', 'LINC01845', 'LINC01847', 'ADRA1B', 'GAPDHP40', 'Y_RNA', 'TTC1'
Annotate tissue_type¶
register_ulabels(cxg_datasets, "tissue_type")
Register collections¶
collection = ln.Collection(
artifacts,
name="cellxgene-census",
version=census_version,
is_new_version_of=ln.Collection.filter(
name="cellxgene-census", version=previous_release
).one(),
)
collection.save()
💡 adding collection ids [456] as inputs for run 27, adding parent transform 19
✅ saved 1 feature set for slot: 'var'
cxg_collections = get_collections_from_cxg()
artifacts.first().key
'cell-census/2024-07-01/h5ads/98e5ea9f-16d6-47ec-a529-686e76515e39.h5ad'
ln.settings.creation.search_names = False
for collection_meta in cxg_collections:
keys = [
f'cell-census/{census_version}/h5ads/{dataset["dataset_id"]}.h5ad'
for dataset in collection_meta["datasets"]
]
collection_artifacts = artifacts.filter(key__in=keys).all()
if collection_artifacts.count() > 0:
kwargs = dict(
name=collection_meta["name"],
description=collection_meta["doi"],
reference=collection_meta["collection_id"],
reference_type="CELLxGENE Collection ID",
version=census_version,
)
collection_record = ln.Collection(
collection_artifacts,
**kwargs,
)
# if is needed here as .save() errors if collection is already saved
if collection_record._state.adding:
collection_record.save()
ln.settings.creation.search_names = True
❗ returning existing collection with same hash: Collection(uid='5LEE4fd5yMXsRtR6WUrs', version='2024-07-01', name='Type I interferon responsive microglia shape cortical development and behavior', description='10.1016/j.cell.2024.02.020', hash='UzOU3URUb50M_5lZtThV', reference='4828d33d-fb26-42e7-bf36-18293b0eec85', reference_type='CELLxGENE Collection ID', visibility=1, created_by_id=1, transform_id=22, run_id=27, updated_at='2024-07-16 12:17:38 UTC')
❗ returning existing collection with same hash: Collection(uid='fYJ7GBbbLRwmVRHyDYu9', version='2024-07-01', name='Human breast cell atlas', description='10.1038/s41588-024-01688-9', hash='wXMzOvp8a-_nGgkwfjSM', reference='48259aa8-f168-4bf5-b797-af8e88da6637', reference_type='CELLxGENE Collection ID', visibility=1, created_by_id=1, transform_id=22, run_id=27, updated_at='2024-07-16 12:17:38 UTC')
❗ returning existing collection with same hash: Collection(uid='6Nu0TAYjj2ePrtyUe6HL', version='2024-07-01', name='SEA-AD: Seattle Alzheimer’s Disease Brain Cell Atlas', description='10.1101/2023.05.08.539485', hash='PDK1s9n30FRoxgRVUV-8', reference='1ca90a2d-2943-483d-b678-b809bf464c30', reference_type='CELLxGENE Collection ID', visibility=1, created_by_id=1, transform_id=22, run_id=27, updated_at='2024-07-16 12:17:40 UTC')
❗ returning existing collection with same hash: Collection(uid='iuZQ5FuxUx8djUoy0wBW', version='2024-07-01', name='Molecular and spatial signatures of mouse brain aging at single-cell resolution', description='10.1016/j.cell.2022.12.010', hash='drimjqzQf_jCXILUH6Qb', reference='31937775-0602-4e52-a799-b6acdd2bac2e', reference_type='CELLxGENE Collection ID', visibility=1, created_by_id=1, transform_id=22, run_id=27, updated_at='2024-07-16 12:17:40 UTC')
❗ returning existing collection with same hash: Collection(uid='XGeEFfpeKAYMtQlnJAaY', version='2024-07-01', name='Multi-scale spatial mapping of cell populations across anatomical sites in healthy human skin and basal cell carcinoma', description='10.1073/pnas.2313326120', hash='SR4yp3Hfk5B3SrqRoNXN', reference='34f12de7-c5e5-4813-a136-832677f98ac8', reference_type='CELLxGENE Collection ID', visibility=1, created_by_id=1, transform_id=22, run_id=27, updated_at='2024-07-16 12:17:41 UTC')
❗ returning existing collection with same hash: Collection(uid='mKfQgOwlCTuLFneySJGV', version='2024-07-01', name='LungMAP — The genomic, epigenomic and biophysical cues controlling the emergence of the gas exchange niche in the lung.', description='10.1126/science.abc3172', hash='51fJjmq_GrC2uLS3Klvm', reference='48d354f5-a5ca-4f35-a3bb-fa3687502252', reference_type='CELLxGENE Collection ID', visibility=1, created_by_id=1, transform_id=22, run_id=27, updated_at='2024-07-16 12:17:41 UTC')
❗ returning existing collection with same hash: Collection(uid='cwFRDKcBVLQ1DgA4O6nC', version='2024-07-01', name='Single cell transcriptomic profiling identifies molecular phenotypes of newborn human lung cells', description='10.3390/genes15030298', hash='P4dNll_9XIdx7s4kAugC', reference='28e9d721-6816-48a2-8d0b-43bf0b0c0ebc', reference_type='CELLxGENE Collection ID', visibility=1, created_by_id=1, transform_id=22, run_id=27, updated_at='2024-07-16 12:17:42 UTC')
❗ returning existing collection with same hash: Collection(uid='jelUsbHyfZH67CXH4Y9I', version='2024-07-01', name='A single-cell transcriptional roadmap of the mouse and human lymph node lymphatic vasculature', description='10.3389/fcvm.2020.00052', hash='nYmNBfcoJd_QfCqr6xUX', reference='9c8808ce-1138-4dbe-818c-171cff10e650', reference_type='CELLxGENE Collection ID', visibility=1, created_by_id=1, transform_id=22, run_id=27, updated_at='2024-07-16 12:17:47 UTC')
❗ returning existing collection with same hash: Collection(uid='Iu3eo1onLtTxVw1jhXA9', version='2024-07-01', name='Type I interferon autoantibodies are associated with systemic immune alterations in patients with COVID-19', description='10.1126/scitranslmed.abh2624', hash='bfClxMqNa1i5FbGt8qjI', reference='7d7cabfd-1d1f-40af-96b7-26a0825a306d', reference_type='CELLxGENE Collection ID', visibility=1, created_by_id=1, transform_id=22, run_id=27, updated_at='2024-07-16 12:17:47 UTC')
❗ returning existing collection with same hash: Collection(uid='kZGohNB7hv9Qu2QlfMe3', version='2024-07-01', name='Insulin is expressed by enteroendocrine cells during human fetal development', description='10.1038/s41591-021-01586-1', hash='_1jCWDd-1qoqGzSTpmZ4', reference='7651ac1a-f947-463a-9223-a9e408a41989', reference_type='CELLxGENE Collection ID', visibility=1, created_by_id=1, transform_id=22, run_id=27, updated_at='2024-07-16 12:17:48 UTC')
❗ returning existing collection with same hash: Collection(uid='tY63QUQc4y2Iv3pC2v4X', version='2024-07-01', name='Multiomic Profiling of Human Clonal Hematopoiesis Reveals Genotype and Cell-Specific Inflammatory Pathway Activation', description='10.1182/bloodadvances.2023011445', hash='YrsiMzRiXI7IML3mmKPo', reference='0aab20b3-c30c-4606-bd2e-d20dae739c45', reference_type='CELLxGENE Collection ID', visibility=1, created_by_id=1, transform_id=22, run_id=27, updated_at='2024-07-16 12:17:49 UTC')
❗ returning existing collection with same hash: Collection(uid='Ioc0kigyLbByHgTGVEdi', version='2024-07-01', name='High-resolution single-cell atlas reveals diversity and plasticity of tumor-associated neutrophils in non-small cell lung cancer', description='10.1016/j.ccell.2022.10.008', hash='sQ2o8xVD_cZ2S9pVIAnU', reference='edb893ee-4066-4128-9aec-5eb2b03f8287', reference_type='CELLxGENE Collection ID', visibility=1, created_by_id=1, transform_id=22, run_id=27, updated_at='2024-07-16 12:17:50 UTC')
❗ returning existing collection with same hash: Collection(uid='7D6yIlnySNfVDZozoMuk', version='2024-07-01', name='Comparative transcriptomics reveals human-specific cortical features', description='10.1126/science.ade9516', hash='6aAYLBJvC-dOgnZxg7sd', reference='4dca242c-d302-4dba-a68f-4c61e7bad553', reference_type='CELLxGENE Collection ID', visibility=1, created_by_id=1, transform_id=22, run_id=27, updated_at='2024-07-16 12:17:50 UTC')
✅ saved 1 feature set for slot: 'var'
✅ saved 1 feature set for slot: 'var'
✅ saved 1 feature set for slot: 'var'
✅ saved 1 feature set for slot: 'var'
✅ saved 1 feature set for slot: 'var'
✅ saved 1 feature set for slot: 'var'
✅ saved 1 feature set for slot: 'var'
✅ saved 1 feature set for slot: 'var'
✅ saved 1 feature set for slot: 'var'
✅ saved 1 feature set for slot: 'var'
✅ saved 1 feature set for slot: 'var'
✅ saved 1 feature set for slot: 'var'
✅ saved 1 feature set for slot: 'var'
✅ saved 1 feature set for slot: 'var'
✅ saved 1 feature set for slot: 'var'
✅ saved 1 feature set for slot: 'var'
✅ saved 1 feature set for slot: 'var'
✅ saved 1 feature set for slot: 'var'
✅ saved 1 feature set for slot: 'var'
✅ saved 1 feature set for slot: 'var'
✅ saved 1 feature set for slot: 'var'
✅ saved 1 feature set for slot: 'var'
✅ saved 1 feature set for slot: 'var'
✅ saved 1 feature set for slot: 'var'
✅ saved 1 feature set for slot: 'var'
✅ saved 1 feature set for slot: 'var'
✅ saved 1 feature set for slot: 'var'
✅ saved 1 feature set for slot: 'var'
✅ saved 1 feature set for slot: 'var'
✅ saved 1 feature set for slot: 'var'
✅ saved 1 feature set for slot: 'var'
✅ saved 1 feature set for slot: 'var'
Add existing collections to their corresponding version families:
collections = ln.Collection.filter(version=census_version).all()
collections.count()
177
collections_previous = ln.Collection.filter(version=previous_release).all()
collections_previous.count()
165
for collection in collections:
collection_previous = collections_previous.filter(
reference=collection.reference
).one_or_none()
if collection_previous is not None:
collection.add_to_version_family(collection_previous, version=census_version)
Show code cell output
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Link metadata to collection¶
collection = ln.Collection.filter(name="cellxgene-census", version=census_version).one()
collection
Collection(uid='dMyEX3NTfKOEYXyMKDD7', version='2024-07-01', name='cellxgene-census', hash='nI8Ag-HANeOpZOz-8CSn', visibility=1, created_by_id=1, transform_id=22, run_id=27, updated_at='2024-07-16 12:24:38 UTC')
feature sets:
collection.describe()
Collection(uid='dMyEX3NTfKOEYXyMKDD7', version='2024-07-01', name='cellxgene-census', hash='nI8Ag-HANeOpZOz-8CSn', visibility=1, updated_at='2024-07-16 12:24:38 UTC')
Provenance
.created_by = 'sunnyosun'
.transform = 'Census release 2024-07-01 (LTS)'
.run = '2024-07-12 12:17:31 UTC'
Feature sets
'obs' = 'assay', 'cell_type', 'development_stage', 'disease', 'donor_id', 'self_reported_ethnicity', 'sex', 'tissue', 'organism', 'tissue_type', 'suspension_type'
'var' = 'DDX11L1', 'WASH7P', 'MIR6859-1', 'MIR1302-2HG', 'MIR1302-2', 'FAM138A', 'OR4G4P', 'OR4G11P', 'OR4F5', 'None', 'CICP27', 'RNU6-1100P', 'DDX11L17', 'WASH9P'
Register the soma store¶
soma_path = f"s3://cellxgene-data-public/cell-census/{census_version}/soma"
ln.UPath(soma_path).view_tree()
14 sub-directories & 6 files with suffixes '', '.tdb'
s3://cellxgene-data-public/cell-census/2024-07-01/soma
├── __tiledb_group.tdb
├── __group/
│ └── __1716234740654_1716234740654_d973bcf54a2242b78bf352eaed600c4a_2
├── __meta/
│ ├── __1716234740654_1716234740654_421b60d8e8ca434a9a3685ac90643161
│ └── __1716234740654_1716234740654_42cb2a544da846dfbe555399fa25c462
├── census_data/
│ ├── __tiledb_group.tdb
│ ├── __group/
│ ├── __meta/
│ ├── homo_sapiens/
│ └── mus_musculus/
└── census_info/
├── __tiledb_group.tdb
├── __group/
├── __meta/
├── datasets/
├── organisms/
├── summary/
└── summary_cell_counts/
soma_artifact = ln.Artifact(soma_path, description=f"Census {census_version}").save()
soma_artifact
💡 path in storage 's3://cellxgene-data-public' with key 'cell-census/2024-07-01/soma'
Artifact(uid='TwIR80MO51oM3CCuFail', description='Census 2024-07-01', key='cell-census/2024-07-01/soma', suffix='', type='dataset', size=870700998221, hash='bzrXBPNvitSVKvb3GG38_w', hash_type='md5-d', n_objects=330, visibility=1, key_is_virtual=False, created_by_id=1, storage_id=2, transform_id=22, run_id=27, updated_at='2024-07-16 12:52:01 UTC')
ln.finish()
❗ cells [(13, 8), (14, 12), (13, 16), (17, 19), (20, 23), (23, 28), (28, 9), (11, None), (None, 16), (16, 18), (18, 23), (23, 25), (25, 27), (27, 29), (32, 34), (35, 7), (7, 9)] were not run consecutively
💡 go to: https://lamin.ai/laminlabs/cellxgene/transform/MIbO3QeJERcy5zKv
💡 if you want to update your notebook without re-running it, use `lamin save notebook.ipynb`