CELLxGENE: scRNA-seq

CZ CELLxGENE hosts the globally largest standardized collection of scRNA-seq datasets.

LaminDB makes it easy to query the CELLxGENE data and integrate it with in-house data of any kind (omics, phenotypes, pdfs, notebooks, ML models, …).

You can use the CELLxGENE data in two ways:

1. Query collections of AnnData objects (this page).

2. Query a big array store produced by concatenated AnnData objects via tiledbsoma (see here).

If you are interested in building similar data assets in-house:

1. See the transfer guide to zero-copy data to your own LaminDB instance.

2. See the scRNA guide for how to create a growing versioned queryable scRNA-seq dataset.

3. See the Curate for validating, curating and registering your own AnnData objects.

Show me a screenshot

Load the public LaminDB instance that mirrors cellxgene:

# !pip install 'lamindb[bionty,jupyter]'
!lamin load laminlabs/cellxgene

💡 connected lamindb: laminlabs/cellxgene

import lamindb as ln
import bionty as bt

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💡 connected lamindb: laminlabs/cellxgene

❗ Full backed capabilities are not available for this version of anndata, please install anndata>=0.9.1.

Query & understand metadata

Auto-complete metadata

You can create look-up objects for any registry in LaminDB, including basic biological entities and things like users or storage locations.

Let’s use auto-complete to look up cell types:

Show me a screenshot

cell_types = bt.CellType.lookup()
cell_types.effector_t_cell

CellType(uid='3nfZTVV4', name='effector T cell', ontology_id='CL:0000911', synonyms='effector T-cell|effector T-lymphocyte|effector T lymphocyte', description='A Differentiated T Cell With Ability To Traffic To Peripheral Tissues And Is Capable Of Mounting A Specific Immune Response.', created_by_id=1, source_id=48, updated_at='2023-11-28 22:30:57 UTC')

You can also arbitrarily chain filters and create lookups from them:

users = ln.User.lookup()
organisms = bt.Organism.lookup()
experimental_factors = bt.ExperimentalFactor.lookup()  # labels for experimental factors
tissues = bt.Tissue.lookup()  # tissue labels
suspension_types = ln.ULabel.filter(name="is_suspension_type").one().children.lookup()  # suspension types

Search & filter metadata

We can use search & filters for metadata:

bt.CellType.search("effector T cell").df().head()

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	uid	name	ontology_id	abbr	synonyms	description	source_id	run_id	created_by_id	updated_at
id
1623	3nfZTVV4	effector T cell	CL:0000911	None	effector T-cell|effector T-lymphocyte|effector...	A Differentiated T Cell With Ability To Traffi...	48	NaN	1	2023-11-28 22:30:57.481778+00:00
1503	1oa5G2Mq	memory T cell	CL:0000813	None	memory T-cell|memory T lymphocyte|memory T-lym...	A Long-Lived, Antigen-Experienced T Cell That ...	48	NaN	1	2023-11-28 22:27:55.580290+00:00
1169	6JD5JCZC	CD8-positive, alpha-beta cytokine secreting ef...	CL:0000908	None	CD8-positive, alpha-beta cytokine secreting ef...	A Cd8-Positive, Alpha-Beta T Cell With The Phe...	48	NaN	1	2023-11-28 22:27:55.571576+00:00
1229	69TEBGqb	exhausted T cell	CL:0011025	None	Tex cell|An effector T cell that displays impa...	None	48	NaN	1	2023-11-28 22:27:55.572884+00:00
1331	43cBCa7s	helper T cell	CL:0000912	None	helper T-lymphocyte|T-helper cell|helper T lym...	A Effector T Cell That Provides Help In The Fo...	48	NaN	1	2023-11-28 22:27:55.575955+00:00

And use a uid to filter exactly one metadata record:

effector_t_cell = bt.CellType.filter(uid="3nfZTVV4").one()
effector_t_cell

CellType(uid='3nfZTVV4', name='effector T cell', ontology_id='CL:0000911', synonyms='effector T-cell|effector T-lymphocyte|effector T lymphocyte', description='A Differentiated T Cell With Ability To Traffic To Peripheral Tissues And Is Capable Of Mounting A Specific Immune Response.', created_by_id=1, source_id=48, updated_at='2023-11-28 22:30:57 UTC')

Understand ontologies

View the related ontology terms:

effector_t_cell.view_parents(distance=2, with_children=True)

Or access them programmatically:

effector_t_cell.children.df()

	uid	name	ontology_id	abbr	synonyms	description	source_id	run_id	created_by_id	updated_at
id
931	2VQirdSp	effector CD8-positive, alpha-beta T cell	CL:0001050	None	effector CD8-positive, alpha-beta T lymphocyte...	A Cd8-Positive, Alpha-Beta T Cell With The Phe...	48	None	1	2023-11-28 22:27:55.565981+00:00
1088	490Xhb24	effector CD4-positive, alpha-beta T cell	CL:0001044	None	effector CD4-positive, alpha-beta T lymphocyte...	A Cd4-Positive, Alpha-Beta T Cell With The Phe...	48	None	1	2023-11-28 22:27:55.569832+00:00
1229	69TEBGqb	exhausted T cell	CL:0011025	None	Tex cell|An effector T cell that displays impa...	None	48	None	1	2023-11-28 22:27:55.572884+00:00
1309	5s4gCMdn	cytotoxic T cell	CL:0000910	None	cytotoxic T lymphocyte|cytotoxic T-lymphocyte|...	A Mature T Cell That Differentiated And Acquir...	48	None	1	2023-11-28 22:27:55.575444+00:00
1331	43cBCa7s	helper T cell	CL:0000912	None	helper T-lymphocyte|T-helper cell|helper T lym...	A Effector T Cell That Provides Help In The Fo...	48	None	1	2023-11-28 22:27:55.575955+00:00

Query artifacts

Unlike in the SOMA guide, here, we’ll query sets of .h5ad files, which correspond to AnnData objects.

ln.Artifact.filter(
    suffix=".h5ad",
    description__contains="immune",
    size__gt=1000000000,
    cell_types__name__in=["B cell", "T cell"],
    created_by__handle="sunnyosun"
).order_by(
    "created_at"
).df(
    include=["cell_types__name", "created_by__handle"]
).head()

	cell_types__name	created_by__handle	uid	version	description	key	suffix	type	accessor	size	...	hash_type	n_objects	n_observations	visibility	key_is_virtual	storage_id	transform_id	run_id	created_by_id	updated_at
879	[conventional dendritic cell, classical monocy...	sunnyosun	BCutg5cxmqLmy2Z5SS8J	2023-07-25	Type I interferon autoantibodies are associate...	cell-census/2023-07-25/h5ads/01ad3cd7-3929-465...	.h5ad	None	AnnData	6353682597	...	md5-n	None	600929	1	False	2	11	16	1	2024-01-24 07:14:10.959155+00:00
1106	[immature B cell, monocyte, naive thymus-deriv...	sunnyosun	3xdOASXuAxxJtSchJO3D	2023-07-25	HSC/immune cells (all hematopoietic-derived ce...	cell-census/2023-07-25/h5ads/48101fa2-1a63-451...	.h5ad	None	AnnData	6214230662	...	md5-n	None	589390	1	False	2	11	16	1	2024-01-24 07:11:10.324135+00:00
1174	[monocyte, conventional dendritic cell, plasma...	sunnyosun	wt7eD72sTzwL3rfYaZr2	2023-07-25	A scRNA-seq atlas of immune cells at the CNS b...	cell-census/2023-07-25/h5ads/58b01044-c5e5-4b0...	.h5ad	None	AnnData	1052158249	...	md5-n	None	130908	1	False	2	11	16	1	2024-01-24 07:09:45.364255+00:00
1377	[monocyte, ciliated cell, macrophage, natural ...	sunnyosun	znTBqWgfYgFlLjdQ6Ba7	2023-07-25	Large-scale single-cell analysis reveals criti...	cell-census/2023-07-25/h5ads/9dbab10c-118d-496...	.h5ad	None	AnnData	13929140098	...	md5-n	None	1462702	1	False	2	11	16	1	2024-01-24 07:14:24.084706+00:00
1482	[effector CD4-positive, alpha-beta T cell, con...	sunnyosun	dEP0dZ8UxLgwnkLjz6Iq	2023-07-25	Single-cell sequencing links multiregional imm...	cell-census/2023-07-25/h5ads/bd65a70f-b274-413...	.h5ad	None	AnnData	1204103287	...	md5-n	None	167283	1	False	2	11	16	1	2024-01-24 07:05:49.602044+00:00

5 rows × 21 columns

Queries by string are easily full of typos. Let’s query with auto-completed records instead:

ln.Artifact.filter(
    suffix=".h5ad",
    description__contains="immune",
    size__gt=1000000000,
    cell_types__in=[cell_types.b_cell, cell_types.t_cell],
    created_by=users.sunnyosun
).order_by(
    "created_at"
).df(
    include=["cell_types__name", "created_by__handle"]
).head()

	cell_types__name	created_by__handle	uid	version	description	key	suffix	type	accessor	size	...	hash_type	n_objects	n_observations	visibility	key_is_virtual	storage_id	transform_id	run_id	created_by_id	updated_at
879	[conventional dendritic cell, classical monocy...	sunnyosun	BCutg5cxmqLmy2Z5SS8J	2023-07-25	Type I interferon autoantibodies are associate...	cell-census/2023-07-25/h5ads/01ad3cd7-3929-465...	.h5ad	None	AnnData	6353682597	...	md5-n	None	600929	1	False	2	11	16	1	2024-01-24 07:14:10.959155+00:00
1106	[immature B cell, monocyte, naive thymus-deriv...	sunnyosun	3xdOASXuAxxJtSchJO3D	2023-07-25	HSC/immune cells (all hematopoietic-derived ce...	cell-census/2023-07-25/h5ads/48101fa2-1a63-451...	.h5ad	None	AnnData	6214230662	...	md5-n	None	589390	1	False	2	11	16	1	2024-01-24 07:11:10.324135+00:00
1174	[monocyte, conventional dendritic cell, plasma...	sunnyosun	wt7eD72sTzwL3rfYaZr2	2023-07-25	A scRNA-seq atlas of immune cells at the CNS b...	cell-census/2023-07-25/h5ads/58b01044-c5e5-4b0...	.h5ad	None	AnnData	1052158249	...	md5-n	None	130908	1	False	2	11	16	1	2024-01-24 07:09:45.364255+00:00
1377	[monocyte, ciliated cell, macrophage, natural ...	sunnyosun	znTBqWgfYgFlLjdQ6Ba7	2023-07-25	Large-scale single-cell analysis reveals criti...	cell-census/2023-07-25/h5ads/9dbab10c-118d-496...	.h5ad	None	AnnData	13929140098	...	md5-n	None	1462702	1	False	2	11	16	1	2024-01-24 07:14:24.084706+00:00
1482	[effector CD4-positive, alpha-beta T cell, con...	sunnyosun	dEP0dZ8UxLgwnkLjz6Iq	2023-07-25	Single-cell sequencing links multiregional imm...	cell-census/2023-07-25/h5ads/bd65a70f-b274-413...	.h5ad	None	AnnData	1204103287	...	md5-n	None	167283	1	False	2	11	16	1	2024-01-24 07:05:49.602044+00:00

5 rows × 21 columns

Query artifacts via collections

To access them, we query the Collection record that links the latest LTS set of .h5ad artifacts:

collection = ln.Collection.filter(name="cellxgene-census", version="2024-07-01").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')

You can get all linked artifacts as a dataframe - there are >1000 h5ad files in cellxgene-census version 2023-12-15.

collection.artifacts.count()

812

collection.artifacts.df().head()  # not tracking run & transform because read-only instance

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	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
3305	1BNWhcCqu1CMSJaHxpbn	2024-07-01	All - A single-cell transcriptomic atlas chara...	cell-census/2024-07-01/h5ads/98e5ea9f-16d6-47e...	.h5ad	dataset	AnnData	2578203515	k-aZJBIjuvnO5Vek3JK-Mg-308	md5-n	None	110824	1	False	2	22	27	1	2024-07-12 12:40:41.719518+00:00
3301	aJTH55LW2CTIWu306YiY	2024-07-01	Supercluster: Deep-layer intratelencephalic	cell-census/2024-07-01/h5ads/98113e7e-f586-406...	.h5ad	dataset	AnnData	3521994530	B8cjeVHgg9Q9Rr-JGaUjfg-420	md5-n	None	228467	1	False	2	22	27	1	2024-07-12 12:40:41.728651+00:00
3313	pnQX4jvkj3eFWGOzDxbW	2024-07-01	Evolution of cellular diversity in primary mot...	cell-census/2024-07-01/h5ads/9b686bb6-1427-4e1...	.h5ad	dataset	AnnData	107509355	Z-uGNA6tRhMB1q46A3R8yg-13	md5-n	None	10739	1	False	2	22	27	1	2024-07-12 12:40:41.762869+00:00
3566	2bF2gDSwbNbDsFVg2KQf	2024-07-01	Supercluster: CGE-derived interneurons	cell-census/2024-07-01/h5ads/e4ddac12-f48f-445...	.h5ad	dataset	AnnData	2586217727	8IDdkinp07n9AgQaWH9yUw-309	md5-n	None	129495	1	False	2	22	27	1	2024-07-12 12:40:42.069642+00:00
2879	Pvhx7GAmAt4SYg03sE0M	2024-07-01	Single nucleus transcriptomic profiling of hum...	cell-census/2024-07-01/h5ads/06ef6b36-6c9b-4e1...	.h5ad	dataset	AnnData	92790726	V9KkecqXGqQJRF1lluo6Kg-12	md5-n	None	10533	1	False	2	22	27	1	2024-07-12 12:34:51.739962+00:00

You can query across artifacts by arbitrary metadata combinations, for instance:

query = collection.artifacts.filter(
    organisms=organisms.human,
    cell_types__in=[cell_types.dendritic_cell, cell_types.neutrophil],
    tissues=tissues.kidney,
    ulabels=suspension_types.cell,
    experimental_factors=experimental_factors.ln_10x_3_v2,
)
query = query.order_by("size")  # order by size
query.df().head()  # convert to DataFrame

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	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
2961	WwmBIhBNLTlRcSoBDt76	2024-07-01	Mature kidney dataset: immune	cell-census/2024-07-01/h5ads/20d87640-4be8-487...	.h5ad	dataset	AnnData	45158726	GCMHkdQSTeXxRVF7gMZFIA-6	md5-n	None	7803	1	False	2	22	27	1	2024-07-12 12:40:43.756335+00:00
2961	WwmBIhBNLTlRcSoBDt76	2024-07-01	Mature kidney dataset: immune	cell-census/2024-07-01/h5ads/20d87640-4be8-487...	.h5ad	dataset	AnnData	45158726	GCMHkdQSTeXxRVF7gMZFIA-6	md5-n	None	7803	1	False	2	22	27	1	2024-07-12 12:40:43.756335+00:00
3000	gHlQ5Muwu3G9pvFCx3x8	2024-07-01	Fetal kidney dataset: immune	cell-census/2024-07-01/h5ads/2d31c0ca-0233-41c...	.h5ad	dataset	AnnData	64546349	2qy8uy-65Sd_XcBU-nrPgA-8	md5-n	None	6847	1	False	2	22	27	1	2024-07-12 12:40:45.273783+00:00
3324	P4Oai3OLGAzRwoicHfLM	2024-07-01	Mature kidney dataset: full	cell-census/2024-07-01/h5ads/9ea768a2-87ab-46b...	.h5ad	dataset	AnnData	194047623	aZVpGZwAfMCziff_5ow2bg-24	md5-n	None	40268	1	False	2	22	27	1	2024-07-12 12:40:44.478948+00:00
3324	P4Oai3OLGAzRwoicHfLM	2024-07-01	Mature kidney dataset: full	cell-census/2024-07-01/h5ads/9ea768a2-87ab-46b...	.h5ad	dataset	AnnData	194047623	aZVpGZwAfMCziff_5ow2bg-24	md5-n	None	40268	1	False	2	22	27	1	2024-07-12 12:40:44.478948+00:00

Query arrays

Each artifact stores an array in form of an curated data matrix, an AnnData object.

Let’s look at the first array in the artifact query and show metadata using .describe():

artifact = query.first()
artifact.describe()

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Artifact(uid='WwmBIhBNLTlRcSoBDt76', version='2024-07-01', description='Mature kidney dataset: immune', key='cell-census/2024-07-01/h5ads/20d87640-4be8-487f-93d4-dce38378d00f.h5ad', suffix='.h5ad', type='dataset', accessor='AnnData', size=45158726, hash='GCMHkdQSTeXxRVF7gMZFIA-6', hash_type='md5-n', n_observations=7803, visibility=1, key_is_virtual=False, updated_at='2024-07-12 12:40:43 UTC')
  Provenance
    .created_by = 'sunnyosun'
    .storage = 's3://cellxgene-data-public'
    .transform = 'Census release 2024-07-01 (LTS)'
    .run = '2024-07-16 12:49:41 UTC'
  Labels
    .organisms = 'human'
    .tissues = 'cortex of kidney', 'renal medulla', 'kidney', 'kidney blood vessel', 'renal pelvis'
    .cell_types = 'classical monocyte', 'plasmacytoid dendritic cell', 'natural killer cell', 'dendritic cell', 'CD4-positive, alpha-beta T cell', 'mast cell', 'neutrophil', 'non-classical monocyte', 'CD8-positive, alpha-beta T cell', 'B cell', ...
    .diseases = 'normal'
    .phenotypes = 'male', 'female'
    .experimental_factors = '10x 3' v2'
    .developmental_stages = '2-year-old human stage', '4-year-old human stage', '12-year-old human stage', '44-year-old human stage', '49-year-old human stage', '53-year-old human stage', '63-year-old human stage', '64-year-old human stage', '67-year-old human stage', '70-year-old human stage', ...
    .ethnicities = 'unknown'
    .ulabels = 'TxK2', 'Wilms1', 'TxK4', 'TTx', 'RCC3', 'RCC1', 'VHL', 'TxK3', 'TxK1', 'Wilms3', ...
  Features
    'donor_id' = 'Wilms3', 'TTx', 'pRCC', 'VHL', 'RCC3', 'TxK1', 'TxK4', 'TxK3', 'RCC2', 'Wilms2', ...
    'organism' = 'human'
    'suspension_type' = 'cell'
  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', 'LINC01845', 'LINC01847', 'ADRA1B', 'TTC1', 'PWWP2A', 'FABP6', 'FABP6-AS1', 'CCNJL', 'C1QTNF2'

More ways of accessing metadata

Access just features:

artifact.features

Or get labels given a feature:

artifact.labels.get(features.tissue).df()

artifact.labels.get(features.collection).one()

If you want to query a slice of the array data, you have two options:

1. Cache & load the entire array into memory via artifact.load() -> AnnData (caches the h5ad on disk, so that you only download once)

2. Stream the array from the cloud using a cloud-backed accessor artifact.open() -> AnnDataAccessor

Both options will run much faster if you run them close to the data (AWS S3 on the US West Coast, consider logging into hosted compute there).

Cache & load:

adata = artifact.load()
adata

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AnnData object with n_obs × n_vars = 7803 × 32839
    obs: 'donor_id', 'donor_age', 'self_reported_ethnicity_ontology_term_id', 'organism_ontology_term_id', 'sample_uuid', 'tissue_ontology_term_id', 'development_stage_ontology_term_id', 'suspension_uuid', 'suspension_type', 'library_uuid', 'assay_ontology_term_id', 'mapped_reference_annotation', 'is_primary_data', 'cell_type_ontology_term_id', 'author_cell_type', 'disease_ontology_term_id', 'reported_diseases', 'sex_ontology_term_id', 'compartment', 'Experiment', 'Project', 'tissue_type', 'cell_type', 'assay', 'disease', 'organism', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage', 'observation_joinid'
    var: 'feature_is_filtered', 'feature_name', 'feature_reference', 'feature_biotype', 'feature_length'
    uns: 'citation', 'default_embedding', 'schema_reference', 'schema_version', 'title'
    obsm: 'X_umap'

Now we have an AnnData object, which stores observation annotations matching our artifact-level query in the .obs slot, and we can re-use almost the same query on the array-level.

See the array-level query

adata_slice = adata[
    adata.obs.cell_type.isin(
        [cell_types.dendritic_cell.name, cell_types.neutrophil.name]
    )
    & (adata.obs.tissue == tissues.kidney.name)
    & (adata.obs.suspension_type == suspension_types.cell.name)
    & (adata.obs.assay == experimental_factors.ln_10x_3_v2.name)
]
adata_slice

See the artifact-level query for comparison

query = collection.artifacts.filter(
    organism=organisms.human,
    cell_types__in=[cell_types.dendritic_cell, cell_types.neutrophil],
    tissues=tissues.kidney,
    ulabels=suspension_types.cell,
    experimental_factors=experimental_factors.ln_10x_3_v2,
)

AnnData uses pandas to manage metadata and the syntax differs slightly. However, the same metadata records are used.

Stream:

adata_backed = artifact.open()
adata_backed

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AnnDataAccessor object with n_obs × n_vars = 7803 × 32839
  constructed for the AnnData object 20d87640-4be8-487f-93d4-dce38378d00f.h5ad
    obs: ['Experiment', 'Project', '_index', 'assay', 'assay_ontology_term_id', 'author_cell_type', 'cell_type', 'cell_type_ontology_term_id', 'compartment', 'development_stage', 'development_stage_ontology_term_id', 'disease', 'disease_ontology_term_id', 'donor_age', 'donor_id', 'is_primary_data', 'library_uuid', 'mapped_reference_annotation', 'observation_joinid', 'organism', 'organism_ontology_term_id', 'reported_diseases', 'sample_uuid', 'self_reported_ethnicity', 'self_reported_ethnicity_ontology_term_id', 'sex', 'sex_ontology_term_id', 'suspension_type', 'suspension_uuid', 'tissue', 'tissue_ontology_term_id', 'tissue_type']
    obsm: ['X_umap']
    raw: ['X', 'var', 'varm']
    uns: ['citation', 'default_embedding', 'schema_reference', 'schema_version', 'title']
    var: ['_index', 'feature_biotype', 'feature_is_filtered', 'feature_length', 'feature_name', 'feature_reference']

We now have an AnnDataAccessor object, which behaves much like an AnnData, and the query looks the same.

See the query

adata_backed_slice = adata_backed[
    adata_backed.obs.cell_type.isin(
        [cell_types.dendritic_cell.name, cell_types.neutrophil.name]
    )
    & (adata_backed.obs.tissue == tissues.kidney.name)
    & (adata_backed.obs.suspension_type == suspension_types.cell.name)
    & (adata_backed.obs.assay == experimental_factors.ln_10x_3_v2.name)
]

adata_backed_slice.to_memory()

Train ML models

You can directly train ML models on very large collections of AnnData objects.

See Train a machine learning model on a collection.

Exploring data by collection

Alternatively,

• you can search a file on the LaminHub UI and fetch it through: ln.Artifact.get(uid)

• or query for a collection you found on CZ CELLxGENE Discover

Let’s search the collections from CELLxGENE within the 2023-12-15 release:

ln.Collection.filter(version="2024-07-01").search("immune human kidney", limit=10)

<QuerySet [Collection(uid='PWDH0VJMkhsYyHwgIhN9', version='2024-07-01', name='A cell atlas of human thymic development defines T cell repertoire formation', description='10.1126/science.aay3224', hash='kdNuiUsjslVtg4wPapRn', reference='de13e3e2-23b6-40ed-a413-e9e12d7d3910', reference_type='CELLxGENE Collection ID', visibility=1, created_by_id=1, transform_id=22, run_id=27, updated_at='2024-07-16 12:24:38 UTC'), Collection(uid='YVo6IaHRKZfDxJLMfiP8', version='2024-07-01', name='Spatial and cell type transcriptional landscape of human cerebellar development', description='10.1038/s41593-021-00872-y', hash='n-9_rjprIyWeU9SFPEtL', reference='1b014f39-f202-45ae-bb7d-9286bddd8d8b', reference_type='CELLxGENE Collection ID', visibility=1, created_by_id=1, transform_id=22, run_id=27, updated_at='2024-07-16 12:24:38 UTC'), 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'), Collection(uid='vUL4bLnfnvI2hRpBSfK5', version='2024-07-01', name='Single-cell transcriptomics of human T cells reveals tissue and activation signatures in health and disease', description='10.1038/s41467-019-12464-3', hash='m4ds_3ZpriUHatzF7H97', reference='24d42e5e-ce6d-45ff-a66b-a3b3b715deaf', reference_type='CELLxGENE Collection ID', visibility=1, created_by_id=1, transform_id=22, run_id=27, updated_at='2024-07-16 12:24:38 UTC'), Collection(uid='lPs6VN8t49wQK3pl71dM', version='2024-07-01', name='A single-cell and spatially resolved atlas of human breast cancers', description='10.1038/s41588-021-00911-1', hash='KfY95LtPo2L8RqP612ug', reference='dea97145-f712-431c-a223-6b5f565f362a', reference_type='CELLxGENE Collection ID', visibility=1, created_by_id=1, transform_id=22, run_id=27, updated_at='2024-07-16 12:18:56 UTC'), Collection(uid='uarP82A6F0cOH8dKjpQL', 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 12:24:38 UTC'), Collection(uid='WpJDkF942c2mHNbJ3En3', version='2024-07-01', name='Cross-tissue immune cell analysis reveals tissue-specific features in humans', description='10.1126/science.abl5197', hash='dPITKafh7mmYOfQqQyWq', reference='62ef75e4-cbea-454e-a0ce-998ec40223d3', reference_type='CELLxGENE Collection ID', visibility=1, created_by_id=1, transform_id=22, run_id=27, updated_at='2024-07-16 12:24:39 UTC'), Collection(uid='rbcRjHfXE0LKIvZcjZro', version='2024-07-01', name='Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations', description='10.1038/s41467-018-06318-7', hash='9f-ccLWu6VqrkN4ITb-Z', reference='bd5230f4-cd76-4d35-9ee5-89b3e7475659', reference_type='CELLxGENE Collection ID', visibility=1, created_by_id=1, transform_id=22, run_id=27, updated_at='2024-07-16 12:24:38 UTC'), Collection(uid='2gBKIwx8AtCHc4nfcQqc', version='2024-07-01', name='A single-cell transcriptome atlas of the adult human retina', description='10.15252/embj.2018100811', hash='sCh4gUTJJJjECsp1dj0q', reference='3472f32d-4a33-48e2-aad5-666d4631bf4c', reference_type='CELLxGENE Collection ID', visibility=1, created_by_id=1, transform_id=22, run_id=27, updated_at='2024-07-16 12:24:39 UTC'), Collection(uid='zZLyhpo1aDdxdbULFbVT', version='2024-07-01', name='Single-cell transcriptomic atlas of the human retina identifies cell types associated with age-related macular degeneration', description='10.1038/s41467-019-12780-8', hash='1B0m9_FahAvefSTM8_AV', reference='1a486c4c-c115-4721-8c9f-f9f096e10857', reference_type='CELLxGENE Collection ID', visibility=1, created_by_id=1, transform_id=22, run_id=27, updated_at='2024-07-16 12:24:38 UTC')]>

Let’s get the record of the top hit collection:

collection = ln.Collection.get("kqiPjpzpK9H9rdtnV67f")
collection

Collection(uid='kqiPjpzpK9H9rdtnV67f', version='2023-12-15', name='Spatiotemporal immune zonation of the human kidney', description='10.1126/science.aat5031', hash='4wGcXeeqsjVdbRdU7ZuJ', reference='120e86b4-1195-48c5-845b-b98054105eec', reference_type='CELLxGENE Collection ID', visibility=1, created_by_id=1, transform_id=17, run_id=22, updated_at='2024-01-29 07:54:33 UTC')

We see it’s a Science paper and we could find more information using the DOI or CELLxGENE collection id.

Check different versions of this collection:

collection.versions.df()

	uid	version	name	description	hash	reference	reference_type	visibility	transform_id	artifact_id	run_id	created_by_id	updated_at
id
17	kqiPjpzpK9H9rdtnHWas	2023-07-25	Spatiotemporal immune zonation of the human ki...	10.1126/science.aat5031	w_VZE7n841ktaA9FjdLh	120e86b4-1195-48c5-845b-b98054105eec	CELLxGENE Collection ID	1	NaN	None	NaN	1	2024-01-08 12:01:20.121095+00:00
365	kqiPjpzpK9H9rdtnV67f	2023-12-15	Spatiotemporal immune zonation of the human ki...	10.1126/science.aat5031	4wGcXeeqsjVdbRdU7ZuJ	120e86b4-1195-48c5-845b-b98054105eec	CELLxGENE Collection ID	1	17.0	None	22.0	1	2024-01-29 07:54:33.854515+00:00
595	kqiPjpzpK9H9rdtnCt1o	2024-07-01	Spatiotemporal immune zonation of the human ki...	10.1126/science.aat5031	I6mGKs5YVdoOJwMdRfj_	120e86b4-1195-48c5-845b-b98054105eec	CELLxGENE Collection ID	1	22.0	None	27.0	1	2024-07-16 12:24:39.167691+00:00

Each collection has at least one Artifact file associated to it. Let’s get the associated artifacts:

collection.artifacts.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
1778	b2x19Eg28GGSNnXW1hAD	2023-12-15	Fetal kidney dataset: nephron	cell-census/2023-12-15/h5ads/08073b32-d389-41f...	.h5ad	None	AnnData	159545411	_JE59jFHDrOn0hj4i1yXSQ-20	md5-n	None	10790	1	False	2	16	22	1	2024-01-29 07:46:06.497662+00:00
1880	WwmBIhBNLTlRcSoBpatT	2023-12-15	Mature kidney dataset: immune	cell-census/2023-12-15/h5ads/20d87640-4be8-487...	.h5ad	None	AnnData	44647761	hSLF-GPhLXaC2tVIOJEdXA-6	md5-n	None	7803	1	False	2	16	22	1	2024-01-29 07:46:33.152678+00:00
1930	gHlQ5Muwu3G9pvFC7egT	2023-12-15	Fetal kidney dataset: immune	cell-census/2023-12-15/h5ads/2d31c0ca-0233-41c...	.h5ad	None	AnnData	64056560	jENeQIq0JdoHl5PyfY-sjA-8	md5-n	None	6847	1	False	2	16	22	1	2024-01-29 07:46:37.205210+00:00
1944	USUgRVwrCMquHiImhk5D	2023-12-15	Mature kidney dataset: non PT parenchyma	cell-census/2023-12-15/h5ads/2fc9c59f-3cfd-48d...	.h5ad	None	AnnData	39294782	3l5iNnBmPFbYfR3-THYWNQ-5	md5-n	None	4620	1	False	2	16	22	1	2024-01-29 07:46:52.173865+00:00
2405	P4Oai3OLGAzRwoicaxCB	2023-12-15	Mature kidney dataset: full	cell-census/2023-12-15/h5ads/9ea768a2-87ab-46b...	.h5ad	None	AnnData	192484358	yghldeu2bOC5jtvnqZH8Og-23	md5-n	None	40268	1	False	2	16	22	1	2024-01-29 07:49:11.905786+00:00
2570	6mnZ3SeQFhffr3wTdZZb	2023-12-15	Fetal kidney dataset: stroma	cell-census/2023-12-15/h5ads/c52de62a-058d-4d7...	.h5ad	None	AnnData	109942751	s24Q5-FNUNQPLZw9BuwOVg-14	md5-n	None	8345	1	False	2	16	22	1	2024-01-29 07:50:01.866851+00:00
2652	11HQaMeIUaOwyHoOWVvA	2023-12-15	Fetal kidney dataset: full	cell-census/2023-12-15/h5ads/d7dcfd8f-2ee7-438...	.h5ad	None	AnnData	341214674	2mnG5TiEpj0Wr5L19TTFRw-41	md5-n	None	27197	1	False	2	16	22	1	2024-01-29 07:50:28.610568+00:00