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 three ways:

1. In the current guide, you’ll see how to query metadata and data based on AnnData objects.

2. If you want to use these in your own LaminDB instance, see the transfer guide.

3. If you’d like to leverage the TileDB-SOMA API for the data subset of CELLxGENE Census, see the Census guide.

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

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

2. See the Annotate for validating, curating and registering your own AnnData objects.

3. Reach out if you are interested in a full zero-copy clone of laminlabs/cellxgene to accelerate building your in-house LaminDB instances.

Setup

Load the public LaminDB instance that mirrors cellxgene on the CLI:

!lamin load laminlabs/cellxgene

💡 connected lamindb: laminlabs/cellxgene

import lamindb as ln
import bionty as bt

💡 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.', updated_at=2023-11-28 22:30:57 UTC, public_source_id=48, created_by_id=1)

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

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")

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	uid	synonyms	score
name
effector T cell	3nfZTVV4	effector T-cell|effector T-lymphocyte|effector...	100.0
ectodermal cell	2rFEBLPn	ectoderm cell	71.4
helper T cell	43cBCa7s	helper T-lymphocyte|T-helper cell|helper T lym...	71.4
memory T cell	1oa5G2Mq	memory T-cell|memory T lymphocyte|memory T-lym...	71.4
sensory receptor cell	6GkjRSiR	receptor cell	71.4
excretory cell	5teqLp2U		69.0
secretory cell	4eEkKmdU		69.0
neurectodermal cell	1eJqfkLq	neurectoderm cell	68.8
pro-T cell	4twkhtZN	pro-T lymphocyte|progenitor T cell	68.8
regulatory T cell	6IELBVIu	regulatory T lymphocyte|Treg|regulatory T-lymp...	68.8
Kupffer cell	5fdXwyLs	hepatic macrophage|macrophagocytus stellatus|l...	66.7
chemoreceptor cell	6wMTbaYL		66.7
follicular B cell	2EhFTUoZ	Fo B cell|follicular B lymphocyte|follicular B...	66.7

bt.CellType.search("CD8-positive cytokine effector T cell")

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	uid	synonyms	score
name
CD8-positive, alpha-beta cytokine secreting effector T cell	6JD5JCZC	CD8-positive, alpha-beta cytokine secreting ef...	77.1
CD4-positive helper T cell	531hEapj	CD4-positive T-helper cell|CD4-positive helper...	69.8
CD8-positive, alpha-beta T cell	6IC9NGJE	CD8-positive, alpha-beta T-cell|CD8-positive, ...	67.6
CD8-positive, alpha-beta cytotoxic T cell	Mv6woHvO	CD8-positive, alpha-beta cytotoxic T-cell|CD8-...	66.7
CD8-positive, alpha-beta memory T cell	7MuNkhO9	CD8-positive, alpha-beta memory T lymphocyte|C...	66.7
CD1c-positive myeloid dendritic cell	5fo7nTlc		65.8
Tc1 cell	4AQr9CRo	Tc1 T lymphocyte|Tc1 T-cell|Tc1 T cell|T-cytot...	65.5
CD141-positive myeloid dendritic cell	CAwwMhIV		64.9
CD4-positive, alpha-beta T cell	4PSMdO3I	CD4-positive, alpha-beta T lymphocyte|CD4-posi...	64.7
CD4-positive, alpha-beta cytotoxic T cell	5zRXDnpu	CD4-positive, alpha-beta cytotoxic T-cell|CD4-...	64.1

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.', updated_at=2023-11-28 22:30:57 UTC, public_source_id=48, created_by_id=1)

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	public_source_id	created_at	updated_at	created_by_id
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	2023-11-28 22:27:55.565976+00:00	2023-11-28 22:27:55.565981+00:00	1
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	2023-11-28 22:27:55.569828+00:00	2023-11-28 22:27:55.569832+00:00	1
1229	69TEBGqb	exhausted T cell	CL:0011025	None	Tex cell|An effector T cell that displays impa...	None	48	2023-11-28 22:27:55.572880+00:00	2023-11-28 22:27:55.572884+00:00	1
1309	5s4gCMdn	cytotoxic T cell	CL:0000910	None	cytotoxic T lymphocyte|cytotoxic T-lymphocyte|...	A Mature T Cell That Differentiated And Acquir...	48	2023-11-28 22:27:55.575440+00:00	2023-11-28 22:27:55.575444+00:00	1
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	2023-11-28 22:27:55.575949+00:00	2023-11-28 22:27:55.575955+00:00	1

Query artifacts

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

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="2023-12-15").one()
collection

Collection(uid='dMyEX3NTfKOEYXyMu591', name='cellxgene-census', version='2023-12-15', hash='0NB32iVKG5ttaW5XILvG', visibility=1, updated_at=2024-01-30 09:09:49 UTC, transform_id=19, run_id=24, created_by_id=1)

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

collection.artifacts.count()

1113

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

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	uid	storage_id	key	suffix	accessor	description	version	size	hash	hash_type	n_objects	n_observations	transform_id	run_id	visibility	key_is_virtual	created_at	updated_at	created_by_id
id
2825	OoktqBIu8jCoGOJlaQPo	2	cell-census/2023-12-15/h5ads/fc0ceb80-d2d9-47c...	.h5ad	AnnData	Sst Chodl - DLPFC: Seattle Alzheimer's Disease...	2023-12-15	73375840	DqV7FraZIIP_l2DJuvHk_g-9	md5-n	None	1877	16	22	1	False	2024-01-11 09:13:25.387366+00:00	2024-01-24 07:18:54.197599+00:00	1
2031	n33nFE2kXSNzNhIAtS3S	2	cell-census/2023-12-15/h5ads/44c83972-e5d2-485...	.h5ad	AnnData	L5 IT - DLPFC: Seattle Alzheimer's Disease Atl...	2023-12-15	4605202922	ztuPyGXWH_OyCq1OyPlNkw-549	md5-n	None	104106	16	22	1	False	2024-01-11 09:13:23.820851+00:00	2024-01-24 07:19:02.027481+00:00	1
1813	mtoOxeGG0Rg3NPH1AlwD	2	cell-census/2023-12-15/h5ads/100c6145-7b0e-4ba...	.h5ad	AnnData	Microglia-PVM - DLPFC: Seattle Alzheimer's Dis...	2023-12-15	634716733	-B96CrmiOANuzE3xU78WsQ-76	md5-n	None	42486	16	22	1	False	2024-01-11 09:13:23.307694+00:00	2024-01-24 07:19:04.190720+00:00	1
1804	V0tqrgE1z1NY2eUUKKQE	2	cell-census/2023-12-15/h5ads/0ed60482-a34f-426...	.h5ad	AnnData	Lamp5 - DLPFC: Seattle Alzheimer's Disease Atl...	2023-12-15	1580667477	xRTDQGA4iOC4r8sSgz53vQ-189	md5-n	None	55968	16	22	1	False	2024-01-11 09:13:23.282158+00:00	2024-01-24 07:19:04.646675+00:00	1
2532	dEP0dZ8UxLgwnkLjHssX	2	cell-census/2023-12-15/h5ads/bd65a70f-b274-413...	.h5ad	AnnData	Single-cell sequencing links multiregional imm...	2023-12-15	1204103287	5hUwdflh_erDK-U2bEzfvw-144	md5-n	None	167283	16	22	1	False	2024-01-11 09:13:24.792407+00:00	2024-01-29 07:49:54.125887+00:00	1

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

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,
)
query = query.order_by("size")  # order by size
query.df().head()  # convert to DataFrame

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	uid	storage_id	key	suffix	accessor	description	version	size	hash	hash_type	n_objects	n_observations	transform_id	run_id	visibility	key_is_virtual	created_at	updated_at	created_by_id
id
1880	WwmBIhBNLTlRcSoBpatT	2	cell-census/2023-12-15/h5ads/20d87640-4be8-487...	.h5ad	AnnData	Mature kidney dataset: immune	2023-12-15	44647761	hSLF-GPhLXaC2tVIOJEdXA-6	md5-n	None	7803	16	22	1	False	2024-01-11 09:13:23.448150+00:00	2024-01-29 07:46:33.152678+00:00	1
1880	WwmBIhBNLTlRcSoBpatT	2	cell-census/2023-12-15/h5ads/20d87640-4be8-487...	.h5ad	AnnData	Mature kidney dataset: immune	2023-12-15	44647761	hSLF-GPhLXaC2tVIOJEdXA-6	md5-n	None	7803	16	22	1	False	2024-01-11 09:13:23.448150+00:00	2024-01-29 07:46:33.152678+00:00	1
1930	gHlQ5Muwu3G9pvFC7egT	2	cell-census/2023-12-15/h5ads/2d31c0ca-0233-41c...	.h5ad	AnnData	Fetal kidney dataset: immune	2023-12-15	64056560	jENeQIq0JdoHl5PyfY-sjA-8	md5-n	None	6847	16	22	1	False	2024-01-11 09:13:23.544310+00:00	2024-01-29 07:46:37.205210+00:00	1
2405	P4Oai3OLGAzRwoicaxCB	2	cell-census/2023-12-15/h5ads/9ea768a2-87ab-46b...	.h5ad	AnnData	Mature kidney dataset: full	2023-12-15	192484358	yghldeu2bOC5jtvnqZH8Og-23	md5-n	None	40268	16	22	1	False	2024-01-11 09:13:24.526987+00:00	2024-01-29 07:49:11.905786+00:00	1
2405	P4Oai3OLGAzRwoicaxCB	2	cell-census/2023-12-15/h5ads/9ea768a2-87ab-46b...	.h5ad	AnnData	Mature kidney dataset: full	2023-12-15	192484358	yghldeu2bOC5jtvnqZH8Og-23	md5-n	None	40268	16	22	1	False	2024-01-11 09:13:24.526987+00:00	2024-01-29 07:49:11.905786+00:00	1

Query arrays

Each artifact stores an array in form of an annotated 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='WwmBIhBNLTlRcSoBpatT', key='cell-census/2023-12-15/h5ads/20d87640-4be8-487f-93d4-dce38378d00f.h5ad', suffix='.h5ad', accessor='AnnData', description='Mature kidney dataset: immune', version='2023-12-15', size=44647761, hash='hSLF-GPhLXaC2tVIOJEdXA-6', hash_type='md5-n', n_observations=7803, visibility=1, key_is_virtual=False, updated_at=2024-01-29 07:46:33 UTC)

Provenance:
  📎 storage: Storage(uid='oIYGbD74', root='s3://cellxgene-data-public', type='s3', region='us-west-2')
  📎 transform: Transform(uid='V4AGIdOJcOgj6K79', name='Census release 2023-12-15 (LTS)', key='cencus-release-2023-12-15-LTS', version='0', type='notebook')
  📎 run: Run(uid='UAAiLAi0BrLvlKnsuvP3', started_at=2024-01-29 07:27:05 UTC, is_consecutive=False)
  📎 created_by: User(uid='kmvZDIX9', handle='sunnyosun', name='Sunny Sun')
  📎 input_of (core.Run): ['2024-01-30 09:07:36 UTC']
Features:
  var: FeatureSet(uid='MLFo2ZBXvibkOyBR9UOR', n=32922, type='number', registry='bionty.Gene')
    'PKIG', 'CXCL6', 'KIAA0586', 'C5orf24', 'MAP4K3', 'ZDHHC7', 'OR2J2', 'PPWD1', 'CHRM4', 'SPRYD3', 'RASSF5', 'PAX3', 'TNFRSF14-AS1', 'RPS10P7', 'TMEM88B', 'WBP1', 'MED28-DT', 'MCCD1', 'RIBC1', 'RAB31', ...
  obs: FeatureSet(uid='zAQ6WnmIMDLslhfgdIOt', name='obs metadata', n=11, type='category', registry='core.Feature')
    🔗 tissue (5, bionty.Tissue): 'kidney', 'kidney blood vessel', 'renal medulla', 'renal pelvis', 'cortex of kidney'
    🔗 tissue_type (0, core.ULabel): 
    🔗 assay (1, bionty.ExperimentalFactor): '10x 3' v2'
    🔗 cell_type (12, bionty.CellType): 'classical monocyte', 'non-classical monocyte', 'neutrophil', 'mature NK T cell', 'kidney resident macrophage', 'CD8-positive, alpha-beta T cell', 'dendritic cell', 'B cell', 'natural killer cell', 'mast cell', ...
    🔗 development_stage (12, bionty.DevelopmentalStage): '49-year-old human stage', '64-year-old human stage', '67-year-old human stage', '72-year-old human stage', '53-year-old human stage', '12-year-old human stage', '19-month-old human stage', '2-year-old human stage', '63-year-old human stage', '4-year-old human stage', ...
    🔗 disease (1, bionty.Disease): 'normal'
    🔗 donor_id (13, core.ULabel): 'pRCC', 'TTx', 'TxK4', 'RCC1', 'Wilms3', 'TxK3', 'TxK2', 'TxK1', 'Wilms1', 'VHL', ...
    🔗 self_reported_ethnicity (1, bionty.Ethnicity): 'unknown'
    🔗 sex (2, bionty.Phenotype): 'male', 'female'
    🔗 suspension_type (1, core.ULabel): 'cell'
    🔗 organism (1, bionty.Organism): 'human'
Labels:
  📎 organism (1, bionty.Organism): 'human'
  📎 tissues (5, bionty.Tissue): 'kidney', 'kidney blood vessel', 'renal medulla', 'renal pelvis', 'cortex of kidney'
  📎 cell_types (12, bionty.CellType): 'classical monocyte', 'non-classical monocyte', 'neutrophil', 'mature NK T cell', 'kidney resident macrophage', 'CD8-positive, alpha-beta T cell', 'dendritic cell', 'B cell', 'natural killer cell', 'mast cell', ...
  📎 diseases (1, bionty.Disease): 'normal'
  📎 phenotypes (2, bionty.Phenotype): 'male', 'female'
  📎 experimental_factors (1, bionty.ExperimentalFactor): '10x 3' v2'
  📎 developmental_stages (12, bionty.DevelopmentalStage): '49-year-old human stage', '64-year-old human stage', '67-year-old human stage', '72-year-old human stage', '53-year-old human stage', '12-year-old human stage', '19-month-old human stage', '2-year-old human stage', '63-year-old human stage', '4-year-old human stage', ...
  📎 ethnicities (1, bionty.Ethnicity): 'unknown'
  📎 ulabels (14, core.ULabel): 'pRCC', 'TTx', 'TxK4', 'RCC1', 'Wilms3', 'TxK3', 'TxK2', 'TxK1', 'Wilms1', 'VHL', ...

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.backed() -> 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 × 32922
    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', 'cell_type', 'assay', 'disease', 'organism', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage'
    var: 'feature_is_filtered', 'feature_name', 'feature_reference', 'feature_biotype'
    uns: 'default_embedding', '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.backed()
adata_backed

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AnnDataAccessor object with n_obs × n_vars = 7803 × 32922
  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', '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']
    obsm: ['X_umap']
    raw: ['X', 'var', 'varm']
    uns: ['default_embedding', 'schema_version', 'title']
    var: ['_index', 'feature_biotype', 'feature_is_filtered', '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 an ML model

You can directly train an ML models on the entire collection.

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="2023-12-15").search("immune human kidney", limit=10)

	uid	score
name
Spatiotemporal immune zonation of the human kidney	kqiPjpzpK9H9rdtnV67f	55.1
The integrated Human Lung Cell Atlas	FaJmPleTV3HjPBTdFyOZ	43.6
Asian Immune Diversity Atlas (AIDA)	ZzUntpjOt8v7Awqdkpja	41.5
Single cell derived mRNA signals across human kidney tumors	Yed6da6CsPXaGmLQDTBi	41.0
Live Human Microglia Single-cell RNA-seq	olY10cghAPIz2oGrQvRC	40.7
Human Brain Cell Atlas v1.0	kDJ9Xb8d11d93LAHZLTC	39.1
Azimuth meta-analysis of human scRNA-seq datasets	RoCAhVTi0ao0p5yX5GZE	38.2
Distinct microbial and immune niches of the human colon	VVsweEynenmLLY85Bvvx	37.8
Human developing neocortex by area	6tT3kRYI2c6slEpvtOWS	37.7
Mapping the developing human immune system across organs	veK7yRfThpdptF4vPT3l	37.3

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

collection = ln.Collection.filter(uid="kqiPjpzpK9H9rdtnV67f").one()

collection

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

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	name	description	version	hash	reference	reference_type	transform_id	run_id	artifact_id	visibility	created_at	updated_at	created_by_id
id
17	kqiPjpzpK9H9rdtnHWas	Spatiotemporal immune zonation of the human ki...	10.1126/science.aat5031	2023-07-25	w_VZE7n841ktaA9FjdLh	120e86b4-1195-48c5-845b-b98054105eec	CELLxGENE Collection ID	NaN	NaN	None	1	2024-01-08 12:01:20.121086+00:00	2024-01-08 12:01:20.121095+00:00	1
365	kqiPjpzpK9H9rdtnV67f	Spatiotemporal immune zonation of the human ki...	10.1126/science.aat5031	2023-12-15	4wGcXeeqsjVdbRdU7ZuJ	120e86b4-1195-48c5-845b-b98054105eec	CELLxGENE Collection ID	17.0	22.0	None	1	2024-01-11 13:41:06.531224+00:00	2024-01-29 07:54:33.854515+00:00	1

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

collection.artifacts.df()

	uid	storage_id	key	suffix	accessor	description	version	size	hash	hash_type	n_objects	n_observations	transform_id	run_id	visibility	key_is_virtual	created_at	updated_at	created_by_id
id
1778	b2x19Eg28GGSNnXW1hAD	2	cell-census/2023-12-15/h5ads/08073b32-d389-41f...	.h5ad	AnnData	Fetal kidney dataset: nephron	2023-12-15	159545411	_JE59jFHDrOn0hj4i1yXSQ-20	md5-n	None	10790	16	22	1	False	2024-01-11 09:13:23.214114+00:00	2024-01-29 07:46:06.497662+00:00	1
1880	WwmBIhBNLTlRcSoBpatT	2	cell-census/2023-12-15/h5ads/20d87640-4be8-487...	.h5ad	AnnData	Mature kidney dataset: immune	2023-12-15	44647761	hSLF-GPhLXaC2tVIOJEdXA-6	md5-n	None	7803	16	22	1	False	2024-01-11 09:13:23.448150+00:00	2024-01-29 07:46:33.152678+00:00	1
1930	gHlQ5Muwu3G9pvFC7egT	2	cell-census/2023-12-15/h5ads/2d31c0ca-0233-41c...	.h5ad	AnnData	Fetal kidney dataset: immune	2023-12-15	64056560	jENeQIq0JdoHl5PyfY-sjA-8	md5-n	None	6847	16	22	1	False	2024-01-11 09:13:23.544310+00:00	2024-01-29 07:46:37.205210+00:00	1
1944	USUgRVwrCMquHiImhk5D	2	cell-census/2023-12-15/h5ads/2fc9c59f-3cfd-48d...	.h5ad	AnnData	Mature kidney dataset: non PT parenchyma	2023-12-15	39294782	3l5iNnBmPFbYfR3-THYWNQ-5	md5-n	None	4620	16	22	1	False	2024-01-11 09:13:23.568572+00:00	2024-01-29 07:46:52.173865+00:00	1
2405	P4Oai3OLGAzRwoicaxCB	2	cell-census/2023-12-15/h5ads/9ea768a2-87ab-46b...	.h5ad	AnnData	Mature kidney dataset: full	2023-12-15	192484358	yghldeu2bOC5jtvnqZH8Og-23	md5-n	None	40268	16	22	1	False	2024-01-11 09:13:24.526987+00:00	2024-01-29 07:49:11.905786+00:00	1
2570	6mnZ3SeQFhffr3wTdZZb	2	cell-census/2023-12-15/h5ads/c52de62a-058d-4d7...	.h5ad	AnnData	Fetal kidney dataset: stroma	2023-12-15	109942751	s24Q5-FNUNQPLZw9BuwOVg-14	md5-n	None	8345	16	22	1	False	2024-01-11 09:13:24.870820+00:00	2024-01-29 07:50:01.866851+00:00	1
2652	11HQaMeIUaOwyHoOWVvA	2	cell-census/2023-12-15/h5ads/d7dcfd8f-2ee7-438...	.h5ad	AnnData	Fetal kidney dataset: full	2023-12-15	341214674	2mnG5TiEpj0Wr5L19TTFRw-41	md5-n	None	27197	16	22	1	False	2024-01-11 09:13:25.042157+00:00	2024-01-29 07:50:28.610568+00:00	1