Link cellxgene-census files to registered metadata¶
import lamindb as ln
import lnschema_bionty as lb
import cellxgene_census
💡 lamindb instance: laminlabs/cellxgene-census
ln.track()
💡 notebook imports: cellxgene-census==1.3.0 lamindb==0.55.0 lnschema_bionty==0.31.1
💡 Transform(id='xE0COcgUvwwtz8', name='Link cellxgene-census files to registered metadata', short_name='files-meta', version='0', type=notebook, updated_at=2023-10-05 14:51:36, created_by_id='kmvZDIX9')
💡 Run(id='8a3C3UexMSvG4UcVeXLD', run_at=2023-10-05 14:51:36, transform_id='xE0COcgUvwwtz8', created_by_id='kmvZDIX9')
census = cellxgene_census.open_soma(census_version="2023-07-25")
The "stable" release is currently 2023-07-25. Specify 'census_version="2023-07-25"' in future calls to open_soma() to ensure data consistency.
2023-10-05 16:53:04,682:INFO - The "stable" release is currently 2023-07-25. Specify 'census_version="2023-07-25"' in future calls to open_soma() to ensure data consistency.
datasets_df = census["census_info"]["datasets"].read().concat().to_pandas()
datasets_df
| soma_joinid | collection_id | collection_name | collection_doi | dataset_id | dataset_title | dataset_h5ad_path | dataset_total_cell_count | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0 | e2c257e7-6f79-487c-b81c-39451cd4ab3c | Spatial multiomics map of trophoblast developm... | 10.1038/s41586-023-05869-0 | f171db61-e57e-4535-a06a-35d8b6ef8f2b | donor_p13_trophoblasts | f171db61-e57e-4535-a06a-35d8b6ef8f2b.h5ad | 31497 |
| 1 | 1 | e2c257e7-6f79-487c-b81c-39451cd4ab3c | Spatial multiomics map of trophoblast developm... | 10.1038/s41586-023-05869-0 | ecf2e08e-2032-4a9e-b466-b65b395f4a02 | All donors trophoblasts | ecf2e08e-2032-4a9e-b466-b65b395f4a02.h5ad | 67070 |
| 2 | 2 | e2c257e7-6f79-487c-b81c-39451cd4ab3c | Spatial multiomics map of trophoblast developm... | 10.1038/s41586-023-05869-0 | 74cff64f-9da9-4b2a-9b3b-8a04a1598040 | All donors all cell states (in vivo) | 74cff64f-9da9-4b2a-9b3b-8a04a1598040.h5ad | 286326 |
| 3 | 3 | f7cecffa-00b4-4560-a29a-8ad626b8ee08 | Mapping single-cell transcriptomes in the intr... | 10.1016/j.ccell.2022.11.001 | 5af90777-6760-4003-9dba-8f945fec6fdf | Single-cell transcriptomic datasets of Renal c... | 5af90777-6760-4003-9dba-8f945fec6fdf.h5ad | 270855 |
| 4 | 4 | 3f50314f-bdc9-40c6-8e4a-b0901ebfbe4c | Single-cell sequencing links multiregional imm... | 10.1016/j.ccell.2021.03.007 | bd65a70f-b274-4133-b9dd-0d1431b6af34 | Single-cell sequencing links multiregional imm... | bd65a70f-b274-4133-b9dd-0d1431b6af34.h5ad | 167283 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 588 | 588 | 180bff9c-c8a5-4539-b13b-ddbc00d643e6 | Molecular characterization of selectively vuln... | 10.1038/s41593-020-00764-7 | f9ad5649-f372-43e1-a3a8-423383e5a8a2 | Molecular characterization of selectively vuln... | f9ad5649-f372-43e1-a3a8-423383e5a8a2.h5ad | 8168 |
| 589 | 589 | a72afd53-ab92-4511-88da-252fb0e26b9a | Single-cell atlas of peripheral immune respons... | 10.1038/s41591-020-0944-y | 456e8b9b-f872-488b-871d-94534090a865 | Single-cell atlas of peripheral immune respons... | 456e8b9b-f872-488b-871d-94534090a865.h5ad | 44721 |
| 590 | 590 | 38833785-fac5-48fd-944a-0f62a4c23ed1 | Construction of a human cell landscape at sing... | 10.1038/s41586-020-2157-4 | 2adb1f8a-a6b1-4909-8ee8-484814e2d4bf | Construction of a human cell landscape at sing... | 2adb1f8a-a6b1-4909-8ee8-484814e2d4bf.h5ad | 598266 |
| 591 | 591 | 5d445965-6f1a-4b68-ba3a-b8f765155d3a | A molecular cell atlas of the human lung from ... | 10.1038/s41586-020-2922-4 | e04daea4-4412-45b5-989e-76a9be070a89 | Krasnow Lab Human Lung Cell Atlas, Smart-seq2 | e04daea4-4412-45b5-989e-76a9be070a89.h5ad | 9409 |
| 592 | 592 | 5d445965-6f1a-4b68-ba3a-b8f765155d3a | A molecular cell atlas of the human lung from ... | 10.1038/s41586-020-2922-4 | 8c42cfd0-0b0a-46d5-910c-fc833d83c45e | Krasnow Lab Human Lung Cell Atlas, 10X | 8c42cfd0-0b0a-46d5-910c-fc833d83c45e.h5ad | 65662 |
593 rows × 8 columns
files = ln.File.filter()
presence_matrix_dict = {}
gene_metadata_dict = {}
no_data = []
for _, row in datasets_df.iloc[499:].iterrows():
print(f"dataset: {row.dataset_id}")
file = files.filter(description__contains=row.dataset_id).one()
# get organism
organism_record = files.first().organism.all().one()
lb.settings.organism = organism_record.name
organism = organism_record.scientific_name
census_data = census["census_data"][organism]
feature_sets = {}
# obs feature set
obs = (
census_data.obs.read(value_filter=f"dataset_id == '{row.dataset_id}'")
.concat()
.to_pandas()
)
if obs.shape[0] == 0:
no_data.append(row.dataset_id)
continue
feature_set_obs = ln.FeatureSet.from_df(
obs.loc[:, ~obs.columns.str.endswith("_id")],
)
feature_sets["obs"] = feature_set_obs
# var feature set
if organism not in presence_matrix_dict:
presence_matrix_dict[organism] = cellxgene_census.get_presence_matrix(
census, organism=organism, measurement_name="RNA"
)
presence_matrix = presence_matrix_dict.get(organism)
var_joinid = presence_matrix[row.soma_joinid, :].tocoo().col
if organism not in gene_metadata_dict:
gene_metadata_dict[organism] = (
census_data.ms["RNA"].var.read().concat().to_pandas()
)
gene_metadata = gene_metadata_dict.get(organism)
var = gene_metadata.loc[gene_metadata.soma_joinid.isin(var_joinid)]
feature_set_var = ln.FeatureSet.from_values(
var.feature_id,
lb.Gene.ensembl_gene_id,
type="number",
)
feature_sets["var"] = feature_set_var
# link two feature sets to file
file._feature_sets = feature_sets
file.save()
# add labels to file
for feature in feature_set_obs.members:
if feature.type == "category":
file.labels.add(obs[feature.name], feature)
Show code cell output
dataset: 1009f384-b12d-448e-ba9f-1b7d2ecfbb4e
dataset: ed852810-a003-4386-9846-1638362cee39
dataset: 9d584fcb-a28a-4b91-a886-ceb66a88ef81
dataset: 78fd69d2-75e4-4207-819a-563139f273c6
dataset: 84f1a631-910b-4fbb-9f76-d915a07316d2
dataset: f75f2ff4-2884-4c2d-b375-70de37a34507
dataset: d4e69e01-3ba2-4d6b-a15d-e7048f78f22e
dataset: 26ae14da-9e5f-4d18-abae-18a5a328feef
dataset: cfa3c355-ee77-4fc8-9a00-78e61d23024c
dataset: 30cd5311-6c09-46c9-94f1-71fe4b91813c
dataset: 21d3e683-80a4-4d9b-bc89-ebb2df513dde
dataset: 774c18c5-efa1-4dc5-9e5e-2c824bab2e34
dataset: 37b21763-7f0f-41ae-9001-60bad6e2841d
dataset: 98e5ea9f-16d6-47ec-a529-686e76515e39
dataset: 48b37086-25f7-4ecd-be66-f5bb378e3aea
dataset: 3f4fe86f-aced-4d10-b174-ee35b9f46b9d
dataset: c9096ac4-ea44-4cf9-82f4-af05cb83eb24
dataset: 170ce19f-7a2f-4926-a1cc-adcad99e7474
dataset: e80d4e1c-672f-496a-8f32-37eab34f727d
dataset: 1d29fd10-c8b3-4611-b0ac-3c578125adbf
dataset: c2878000-d3f0-4d30-9a8a-2139a13c72f8
dataset: e3b8c485-7811-407e-99ed-c7d574be9d7c
dataset: 821c79aa-044e-40cf-b331-0fe3edd48019
dataset: 0bd1a1de-3aee-40e0-b2ec-86c7a30c7149
dataset: bc7466d7-ff13-4ff2-9c3d-7a1d208bd492
dataset: 05e6f6e3-0473-4b85-9f94-bcc5f1b5e04b
dataset: 4546e757-34d0-4d17-be06-538318925fcd
dataset: 2491629a-bde0-46ad-a073-e34fcb516857
dataset: 1efd4700-87dd-4b45-8762-11ba3fea7a65
dataset: a6626b73-a0de-4dee-99aa-2559ab05af11
dataset: 66ff82b4-9380-469c-bc4b-cfa08eacd325
dataset: c08f8441-4a10-4748-872a-e70c0bcccdba
dataset: 01209dce-3575-4bed-b1df-129f57fbc031
dataset: ec6c52b8-3368-4f72-a416-1ade0dab97bf
dataset: 524179b0-b406-4723-9c46-293ffa77ca81
dataset: cbd62079-bed8-4aa1-9659-670f9cb51f9d
dataset: bf12f9c6-4211-4c91-9c71-22019f29f516
dataset: 8f1bc86b-7976-4826-8602-f5266160ad86
dataset: 0380ddce-c31b-422a-88fe-34a1945bd949
dataset: 1a0610d8-1339-479b-b261-7fb586c3dab9
dataset: 98ad5247-68f8-42f8-b8e5-7938cb373a91
dataset: 6e4f871d-fd7c-4909-8c14-e4c9957c2e8f
dataset: e2b469d4-b5c3-4a35-9d19-ee71ce61cae0
dataset: 4fa55ee5-8da4-4d42-9525-1c52d4ce50bf
dataset: ef47280b-3e68-4188-a49a-7b8374c8a6f2
dataset: db55b719-6102-493a-9251-404bc501d0de
dataset: 7c6091da-4606-44c7-a2c4-ef896de09e28
dataset: 0fb7916e-7a68-4a4c-a441-3ab3989f29a7
dataset: 6202a243-b713-4e12-9ced-c387f8483dea
dataset: a7ace090-1ba1-47f2-8def-6e11298b7816
dataset: de4e7a0c-91b2-44e4-b382-87da74c9efb6
dataset: b8c618e5-4b3d-4566-8a3f-7e40047f5c54
dataset: 1fe63353-9e75-4824-aa30-ed8d84be748c
dataset: 76544818-bc5b-4a0d-87d4-40dde89545cb
dataset: 93966790-bbfa-420f-aa85-bc5ca51d9c96
dataset: 35081d47-99bf-4507-9541-735428df9a9f
dataset: 1304e107-0f06-4d33-b634-d95ed986d02b
dataset: d622cee4-56e1-44ba-8b05-fd2f0f2032e6
dataset: a9affc92-a291-4eb9-996f-147392132323
dataset: 50d79de5-bd17-4d14-a295-199d71ff56be
dataset: 5e765f97-1cf1-407e-a86c-e28701f4749d
dataset: c88e2a9c-72b8-4a88-a2f6-e428eada0c86
dataset: 3a15ab1c-c36c-4842-9a3e-47e6ffd0ba6f
dataset: bc2a7b3d-f04e-477e-96c9-9d5367d5425c
dataset: e763ed0d-0e5a-4b8e-9514-6da3d9e47956
dataset: db0752b9-f20e-40b8-8997-992f3ae0bb2e
dataset: 055ca631-6ffb-40de-815e-b931e10718c0
dataset: 4c4cd77c-8fee-4836-9145-16562a8782fe
dataset: d9b4bc69-ed90-4f5f-99b2-61b0681ba436
dataset: 59b69042-47c2-47fd-ad03-d21beb99818f
dataset: e0ed3c55-aff6-4bb7-b6ff-98a2d90b890c
dataset: ae5341b8-60fb-4fac-86db-86e49ee66287
dataset: 28c696bb-9549-434b-9340-dc745a846f9a
dataset: 9df60c57-fdf3-4e93-828e-fe9303f20438
dataset: f7a068f1-0fdb-48e8-8029-db870ff11d9e
dataset: 9b686bb6-1427-4e13-b451-7ee961115cf9
dataset: b6203114-e133-458a-aed5-eed1028378b4
dataset: 6acb6637-ac08-4a65-b2d1-581e51dc7ccf
dataset: 6c600df6-ddca-4628-a8bb-1d6de1e3f9b4
dataset: bdacc907-7c26-419f-8808-969eab3ca2e8
dataset: 24066994-8183-488d-b037-ef6bb524af39
dataset: 873ff933-4fda-4936-9a70-67df11af90ac
dataset: 2727d83a-0af0-443a-bff8-58dc7028289a
dataset: b94e3bdf-a385-49cc-b312-7a63cc28b77a
dataset: 75a881cf-5d88-46e2-bf9b-97e5cbc1bd56
dataset: cd77258f-b08b-4c89-b93f-6e6f146b1a4d
dataset: 06b91002-4d3d-4d2e-8484-20c3b31e232c
dataset: 1492eb6b-7d50-4c4d-94ac-c801a7d5555c
dataset: 9f1049ac-f8b7-45ad-8e31-6e96c3e5058f
dataset: f9ad5649-f372-43e1-a3a8-423383e5a8a2
dataset: 456e8b9b-f872-488b-871d-94534090a865
dataset: 2adb1f8a-a6b1-4909-8ee8-484814e2d4bf
dataset: e04daea4-4412-45b5-989e-76a9be070a89
dataset: 8c42cfd0-0b0a-46d5-910c-fc833d83c45e
file.describe()
File(id='0sbCRBKbqkEuSjhzfp42', key='cell-census/2023-07-25/h5ads/8c42cfd0-0b0a-46d5-910c-fc833d83c45e.h5ad', suffix='.h5ad', accessor='AnnData', description='Krasnow Lab Human Lung Cell Atlas, 10X|8c42cfd0-0b0a-46d5-910c-fc833d83c45e', size=588959280, hash='N0yW4Iksvgw93PzdE_4M0w-71', hash_type='md5-n', updated_at=2023-10-05 20:55:51)
Provenance:
🗃️ storage: Storage(id='oIYGbD74', root='s3://cellxgene-data-public', type='s3', region='us-west-2', updated_at=2023-09-19 13:17:56, created_by_id='kmvZDIX9')
📔 transform: Transform(id='nhGTqlIHEyn7z8', name='Register h5ad files of cellxgene-census', short_name='files', version='0', type='notebook', reference='https://github.com/laminlabs/cellxgene-census-lamin/blob/2553c2690909976efe380ca96d9e4d6b9a6c6749/docs/notebooks/datasets.ipynb', reference_type='github', updated_at=2023-10-05 14:04:28, created_by_id='kmvZDIX9')
👣 run: Run(id='60jqKpxivkwkpEFZr8mp', run_at=2023-10-05 18:56:43, transform_id='nhGTqlIHEyn7z8', created_by_id='kmvZDIX9')
👤 created_by: User(id='kmvZDIX9', handle='sunnyosun', email='xs338@nyu.edu', name='Sunny Sun', updated_at=2023-09-19 14:58:33)
Features:
external: FeatureSet(id='OHD9LSDGO1FtSWUtcpqG', n=2, registry='core.Feature', hash='NspE1QMvOo8aoOOrotmH', updated_at=2023-10-05 16:06:49, modality_id='FyZj4S3Z', created_by_id='kmvZDIX9')
🔗 organism (1, bionty.Species): 'human'
🔗 collection (1, core.ULabel): 'A molecular cell atlas of the human lung from single cell RNA sequencing'
obs: FeatureSet(id='BWCLXmm3DHxuCDjIQOCq', n=11, registry='core.Feature', hash='2t62zxs1sYVD06pTg5f-', updated_at=2023-10-05 16:34:55, modality_id='FyZj4S3Z', created_by_id='kmvZDIX9')
is_primary_data (bool)
🔗 disease (1, bionty.Disease): 'normal'
🔗 sex (2, bionty.Phenotype): 'male', 'female'
🔗 assay (1, bionty.ExperimentalFactor): '10x 3' v2'
🔗 self_reported_ethnicity (1, bionty.Ethnicity): 'unknown'
soma_joinid (int)
🔗 tissue_general (2, bionty.Tissue): 'blood', 'lung'
🔗 cell_type (46, bionty.CellType): 'CD1c-positive myeloid dendritic cell', 'endothelial cell of artery', 'endothelial cell', 'myeloid dendritic cell, human', 'bronchial smooth muscle cell', 'mature NK T cell', 'myofibroblast cell', 'respiratory basal cell', 'classical monocyte', 'pulmonary interstitial fibroblast', ...
🔗 suspension_type (1, core.ULabel): 'cell'
🔗 tissue (2, bionty.Tissue): 'blood', 'lung'
🔗 development_stage (3, bionty.DevelopmentalStage): '51-year-old stage', '46-year-old stage', '75-year-old stage'
var: FeatureSet(id='Jh8na7zoGr4zRLo7LMWX', n=20921, type='number', registry='bionty.Gene', hash='SESOtBCQ_cY63UiFYdvz', updated_at=2023-10-05 20:55:42, modality_id='xdH6Qmmr', created_by_id='kmvZDIX9')
'P4HA2-AS1', 'ASPSCR1', 'SYT9', 'THSD1', 'C1QL4', 'PDGFA-DT', 'MIER2', 'BVES', 'GDPD4', 'KCNG3', 'LINC02681', 'MMD', 'JPH4', 'C8orf89', 'RBM15B', 'FBXO27', 'RNVU1-4', 'OGA', 'ELOA2', 'ETFBKMT', ...
Labels:
🏷️ organism (1, bionty.Species): 'human'
🏷️ tissues (2, bionty.Tissue): 'blood', 'lung'
🏷️ cell_types (46, bionty.CellType): 'CD1c-positive myeloid dendritic cell', 'endothelial cell of artery', 'endothelial cell', 'myeloid dendritic cell, human', 'bronchial smooth muscle cell', 'mature NK T cell', 'myofibroblast cell', 'respiratory basal cell', 'classical monocyte', 'pulmonary interstitial fibroblast', ...
🏷️ diseases (1, bionty.Disease): 'normal'
🏷️ phenotypes (2, bionty.Phenotype): 'male', 'female'
🏷️ experimental_factors (1, bionty.ExperimentalFactor): '10x 3' v2'
🏷️ developmental_stages (3, bionty.DevelopmentalStage): '51-year-old stage', '46-year-old stage', '75-year-old stage'
🏷️ ethnicities (1, bionty.Ethnicity): 'unknown'
🏷️ ulabels (2, core.ULabel): 'A molecular cell atlas of the human lung from single cell RNA sequencing', 'cell'
no data datasets¶
Querying the following datasets from census didn’t result any data:
no_data
Show code cell output
['d7291f04-fbbb-4d65-990a-f01fa44e915b',
'be46dfdc-0f99-4731-8957-64ca37364985',
'a13bda79-9134-46c9-9ed1-a2858be9aafe',
'5695d556-974e-4d92-9e99-5f61b8695313',
'12967895-3d58-4e93-be2c-4e1bcf4388d5',
'49e4ffcc-5444-406d-bdee-577127404ba8',
'3bbb6cf9-72b9-41be-b568-656de6eb18b5',
'58b01044-c5e5-4b0f-8a2d-6ebf951e01ff',
'a539c7af-fb65-44b1-8812-000b097eac99',
'6347cc90-f284-41d8-a131-db4a37bd796f',
'dbb4e1ed-d820-4e83-981f-88ef7eb55a35',
'f71f046d-ea2d-4470-ac38-572cbb06ee12',
'a14a995b-2f1d-40df-8f61-ab2754cac630',
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'79a2344d-eddd-45b1-b376-39eddfab1899',
'76b604f3-bb29-4db7-be2b-6500dc5fbdb7',
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'cb5efdb0-f91c-4cbd-9ad4-9d4fa41c572d',
'9bb9596d-f23f-4558-912f-d4dc7d52721b',
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'2a262b59-7936-4ecd-b656-248247a0559f',
'26ae14da-9e5f-4d18-abae-18a5a328feef',
'98e5ea9f-16d6-47ec-a529-686e76515e39',
'48b37086-25f7-4ecd-be66-f5bb378e3aea',
'3f4fe86f-aced-4d10-b174-ee35b9f46b9d',
'c9096ac4-ea44-4cf9-82f4-af05cb83eb24',
'170ce19f-7a2f-4926-a1cc-adcad99e7474',
'e80d4e1c-672f-496a-8f32-37eab34f727d',
'1d29fd10-c8b3-4611-b0ac-3c578125adbf',
'c2878000-d3f0-4d30-9a8a-2139a13c72f8',
'e3b8c485-7811-407e-99ed-c7d574be9d7c',
'821c79aa-044e-40cf-b331-0fe3edd48019',
'0bd1a1de-3aee-40e0-b2ec-86c7a30c7149',
'bc7466d7-ff13-4ff2-9c3d-7a1d208bd492',
'05e6f6e3-0473-4b85-9f94-bcc5f1b5e04b',
'4546e757-34d0-4d17-be06-538318925fcd',
'2491629a-bde0-46ad-a073-e34fcb516857',
'1efd4700-87dd-4b45-8762-11ba3fea7a65',
'a6626b73-a0de-4dee-99aa-2559ab05af11',
'66ff82b4-9380-469c-bc4b-cfa08eacd325',
'c08f8441-4a10-4748-872a-e70c0bcccdba',
'ec6c52b8-3368-4f72-a416-1ade0dab97bf',
'524179b0-b406-4723-9c46-293ffa77ca81',
'cbd62079-bed8-4aa1-9659-670f9cb51f9d',
'bf12f9c6-4211-4c91-9c71-22019f29f516',
'8f1bc86b-7976-4826-8602-f5266160ad86',
'0380ddce-c31b-422a-88fe-34a1945bd949',
'1a0610d8-1339-479b-b261-7fb586c3dab9',
'98ad5247-68f8-42f8-b8e5-7938cb373a91',
'6e4f871d-fd7c-4909-8c14-e4c9957c2e8f',
'e2b469d4-b5c3-4a35-9d19-ee71ce61cae0',
'4fa55ee5-8da4-4d42-9525-1c52d4ce50bf',
'ef47280b-3e68-4188-a49a-7b8374c8a6f2',
'db55b719-6102-493a-9251-404bc501d0de',
'7c6091da-4606-44c7-a2c4-ef896de09e28',
'0fb7916e-7a68-4a4c-a441-3ab3989f29a7',
'6202a243-b713-4e12-9ced-c387f8483dea',
'a7ace090-1ba1-47f2-8def-6e11298b7816',
'de4e7a0c-91b2-44e4-b382-87da74c9efb6',
'b8c618e5-4b3d-4566-8a3f-7e40047f5c54',
'1fe63353-9e75-4824-aa30-ed8d84be748c',
'76544818-bc5b-4a0d-87d4-40dde89545cb',
'93966790-bbfa-420f-aa85-bc5ca51d9c96',
'35081d47-99bf-4507-9541-735428df9a9f',
'1304e107-0f06-4d33-b634-d95ed986d02b',
'd622cee4-56e1-44ba-8b05-fd2f0f2032e6',
'a9affc92-a291-4eb9-996f-147392132323',
'50d79de5-bd17-4d14-a295-199d71ff56be',
'5e765f97-1cf1-407e-a86c-e28701f4749d',
'c88e2a9c-72b8-4a88-a2f6-e428eada0c86',
'3a15ab1c-c36c-4842-9a3e-47e6ffd0ba6f',
'e0ed3c55-aff6-4bb7-b6ff-98a2d90b890c',
'28c696bb-9549-434b-9340-dc745a846f9a']
len(no_data)
82