Validate & register scRNA-seq datasets

scRNA-seq measures gene expression of individual cells.

Their analysis is typically based on data objects like AnnData, SingleCellExperiment & Seurat objects.

These objects often contain non-validated metadata, making data integration & interpretation hard.

In this notebook, LaminDB is used to turn AnnData objects into validated & queryable assets.

Setup

!lamin init --storage ./test-scrna --schema bionty

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💡 creating schemas: core==0.47.5 bionty==0.30.4 
✅ saved: User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-09-06 17:22:06)
✅ saved: Storage(id='kSdUg2Cl', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/test-scrna', type='local', updated_at=2023-09-06 17:22:06, created_by_id='DzTjkKse')

✅ loaded instance: testuser1/test-scrna
💡 did not register local instance on hub (if you want, call `lamin register`)

import lamindb as ln
import lnschema_bionty as lb
import pandas as pd

ln.track()

✅ loaded instance: testuser1/test-scrna (lamindb 0.52.2)

💡 notebook imports: lamindb==0.52.2 lnschema_bionty==0.30.4 pandas==1.5.3

✅ saved: Transform(id='Nv48yAceNSh8z8', name='Validate & register scRNA-seq datasets', short_name='scrna', version='0', type=notebook, updated_at=2023-09-06 17:22:08, created_by_id='DzTjkKse')

✅ saved: Run(id='vqkf0y8uN3Qdq8ROUKih', run_at=2023-09-06 17:22:08, transform_id='Nv48yAceNSh8z8', created_by_id='DzTjkKse')

Human immune cells: Conde22

lb.settings.species = "human"

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Access

Let’s look at a scRNA-seq count matrix in form of an AnnData object that we’d like to ingest into LaminDB:

adata = ln.dev.datasets.anndata_human_immune_cells(
    populate_registries=True  # this pre-populates registries
)

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adata

AnnData object with n_obs × n_vars = 1648 × 36503
    obs: 'donor', 'tissue', 'cell_type', 'assay'
    var: 'feature_is_filtered', 'feature_reference', 'feature_biotype'
    uns: 'cell_type_ontology_term_id_colors', 'default_embedding', 'schema_version', 'title'
    obsm: 'X_umap'

This AnnData object does not require filtering, normalizing or formatting, hence, there is no step.

Validate

Validate genes in .var

lb.Gene.validate(adata.var.index, lb.Gene.ensembl_gene_id);

✅ 36355 terms (99.60%) are validated for ensembl_gene_id

❗ 148 terms (0.40%) are not validated for ensembl_gene_id: ENSG00000269933, ENSG00000261737, ENSG00000259834, ENSG00000256374, ENSG00000263464, ENSG00000203812, ENSG00000272196, ENSG00000272880, ENSG00000270188, ENSG00000287116, ENSG00000237133, ENSG00000224739, ENSG00000227902, ENSG00000239467, ENSG00000272551, ENSG00000280374, ENSG00000236886, ENSG00000229352, ENSG00000286601, ENSG00000227021, ...

148 gene identifiers can’t be validated (not currently in the Gene registry). Lt’s inspect them to see what to do:

inspector = lb.Gene.inspect(adata.var.index, lb.Gene.ensembl_gene_id)

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✅ 36355 terms (99.60%) are validated for ensembl_gene_id

❗ 148 terms (0.40%) are not validated for ensembl_gene_id: ENSG00000269933, ENSG00000261737, ENSG00000259834, ENSG00000256374, ENSG00000263464, ENSG00000203812, ENSG00000272196, ENSG00000272880, ENSG00000270188, ENSG00000287116, ENSG00000237133, ENSG00000224739, ENSG00000227902, ENSG00000239467, ENSG00000272551, ENSG00000280374, ENSG00000236886, ENSG00000229352, ENSG00000286601, ENSG00000227021, ...

💡    detected 35 Gene terms in Bionty for ensembl_gene_id: 'ENSG00000277836', 'ENSG00000198695', 'ENSG00000271254', 'ENSG00000277400', 'ENSG00000198899', 'ENSG00000274175', 'ENSG00000212907', 'ENSG00000275869', 'ENSG00000276017', 'ENSG00000228253', 'ENSG00000277196', 'ENSG00000273554', 'ENSG00000274792', 'ENSG00000198727', 'ENSG00000278704', 'ENSG00000277630', 'ENSG00000198804', 'ENSG00000274847', 'ENSG00000277856', 'ENSG00000278384', ...

💡 →  add records from Bionty to your {model_name} registry via .from_values()

💡    couldn't validate 113 terms: 'ENSG00000270394', 'ENSG00000259855', 'ENSG00000263464', 'ENSG00000269900', 'ENSG00000227220', 'ENSG00000203812', 'ENSG00000272551', 'ENSG00000226380', 'ENSG00000237513', 'ENSG00000256892', 'ENSG00000259834', 'ENSG00000256618', 'ENSG00000236740', 'ENSG00000261534', 'ENSG00000256427', 'ENSG00000272880', 'ENSG00000204092', 'ENSG00000273576', 'ENSG00000239665', 'ENSG00000285106', ...

💡 →  if you are sure, create new records via ln.Gene() and save to your registry

Logging says 35 of the non-validated ids can be found in the Bionty reference. Let’s register them:

records = lb.Gene.from_values(inspector.non_validated, lb.Gene.ensembl_gene_id)
ln.save(records)

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✅ created 35 Gene records from Bionty matching ensembl_gene_id: 'ENSG00000198804', 'ENSG00000198712', 'ENSG00000228253', 'ENSG00000198899', 'ENSG00000198938', 'ENSG00000198840', 'ENSG00000212907', 'ENSG00000198886', 'ENSG00000198786', 'ENSG00000198695', 'ENSG00000198727', 'ENSG00000278704', 'ENSG00000277400', 'ENSG00000274847', 'ENSG00000276256', 'ENSG00000277630', 'ENSG00000278384', 'ENSG00000273748', 'ENSG00000271254', 'ENSG00000277475', ...

❗ did not create Gene records for 113 non-validated ensembl_gene_ids: 'ENSG00000112096', 'ENSG00000182230', 'ENSG00000203812', 'ENSG00000204092', 'ENSG00000215271', 'ENSG00000221995', 'ENSG00000224739', 'ENSG00000224745', 'ENSG00000225932', 'ENSG00000226377', 'ENSG00000226380', 'ENSG00000226403', 'ENSG00000227021', 'ENSG00000227220', 'ENSG00000227902', 'ENSG00000228139', 'ENSG00000228906', 'ENSG00000229352', 'ENSG00000231575', 'ENSG00000232196', ...

The remaining 113 are legacy IDs, not present in the current Ensembl assembly (e.g. ENSG00000112096).

We’d still like to register them:

validated = lb.Gene.validate(adata.var.index, lb.Gene.ensembl_gene_id)
records = [lb.Gene(ensembl_gene_id=id) for id in adata.var.index[~validated]]
ln.save(records)

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✅ 36390 terms (99.70%) are validated for ensembl_gene_id

❗ 113 terms (0.30%) are not validated for ensembl_gene_id: ENSG00000269933, ENSG00000261737, ENSG00000259834, ENSG00000256374, ENSG00000263464, ENSG00000203812, ENSG00000272196, ENSG00000272880, ENSG00000270188, ENSG00000287116, ENSG00000237133, ENSG00000224739, ENSG00000227902, ENSG00000239467, ENSG00000272551, ENSG00000280374, ENSG00000236886, ENSG00000229352, ENSG00000286601, ENSG00000227021, ...

Now all genes pass validation:

lb.Gene.validate(adata.var.index, lb.Gene.ensembl_gene_id);

✅ 36503 terms (100.00%) are validated for ensembl_gene_id

Validate metadata in .obs

adata.obs.columns

Index(['donor', 'tissue', 'cell_type', 'assay'], dtype='object')

ln.Feature.validate(adata.obs.columns);

✅ 3 terms (75.00%) are validated for name

❗ 1 term (25.00%) is not validated for name: donor

1 feature is not validated: "donor". Let’s register it:

Tip

Use features = ln.Feature.from_df(df) to bulk create features with types.

feature = ln.Feature(name="donor", type="category")
ln.save(feature)

All metadata columns are now validated as feature:

ln.Feature.validate(adata.obs.columns);

✅ 4 terms (100.00%) are validated for name

Next, let’s validate the corresponding labels of each feature.

Some of the metadata labels can be typed using dedicated registries like CellType:

validated = lb.CellType.validate(adata.obs.cell_type)

❗ received 32 unique terms, 1616 empty/duplicated terms are ignored

✅ 30 terms (93.80%) are validated for name

❗ 2 terms (6.20%) are not validated for name: germinal center B cell, megakaryocyte

Register non-validated cell types - they can all be loaded from a public ontology through Bionty:

records = lb.CellType.from_values(adata.obs.cell_type[~validated], "name")
ln.save(records)

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✅ created 2 CellType records from Bionty matching name: 'germinal center B cell', 'megakaryocyte'

❗ now recursing through parents: this only happens once, but is much slower than bulk saving

💡 you can switch this off via: lb.settings.auto_save_parents = False

💡 also saving parents of CellType(id='uMLhrmbZ', name='germinal center B cell', ontology_id='CL:0000844', synonyms='GC B-cell|GC B cell|GC B lymphocyte|germinal center B lymphocyte|GC B-lymphocyte|germinal center B-cell|germinal center B-lymphocyte', description='A Rapidly Cycling Mature B Cell That Has Distinct Phenotypic Characteristics And Is Involved In T-Dependent Immune Responses And Located Typically In The Germinal Centers Of Lymph Nodes. This Cell Type Expresses Ly77 After Activation.', updated_at=2023-09-06 17:22:35, bionty_source_id='wbOD', created_by_id='DzTjkKse')

✅ created 1 CellType record from Bionty matching ontology_id: 'CL:0000785'

❗ now recursing through parents: this only happens once, but is much slower than bulk saving

💡 you can switch this off via: lb.settings.auto_save_parents = False

💡 also saving parents of CellType(id='0I51jgPp', name='mature B cell', ontology_id='CL:0000785', synonyms='mature B lymphocyte|mature B-cell|mature B-lymphocyte', description='A B Cell That Is Mature, Having Left The Bone Marrow. Initially, These Cells Are Igm-Positive And Igd-Positive, And They Can Be Activated By Antigen.', updated_at=2023-09-06 17:22:35, bionty_source_id='wbOD', created_by_id='DzTjkKse')

✅ created 1 CellType record from Bionty matching ontology_id: 'CL:0001201'

❗ now recursing through parents: this only happens once, but is much slower than bulk saving

💡 you can switch this off via: lb.settings.auto_save_parents = False

💡 also saving parents of CellType(id='CIS4VJI0', name='B cell, CD19-positive', ontology_id='CL:0001201', synonyms='CD19+ B cell|B lymphocyte, CD19-positive|B-lymphocyte, CD19-positive|CD19-positive B cell|B-cell, CD19-positive', description='A B Cell That Is Cd19-Positive.', updated_at=2023-09-06 17:22:36, bionty_source_id='wbOD', created_by_id='DzTjkKse')

✅ created 1 CellType record from Bionty matching ontology_id: 'CL:0000236'

❗ now recursing through parents: this only happens once, but is much slower than bulk saving

💡 you can switch this off via: lb.settings.auto_save_parents = False

💡 also saving parents of CellType(id='cx8VcggA', name='B cell', ontology_id='CL:0000236', synonyms='B-cell|B lymphocyte|B-lymphocyte', description='A Lymphocyte Of B Lineage That Is Capable Of B Cell Mediated Immunity.', updated_at=2023-09-06 17:22:36, bionty_source_id='wbOD', created_by_id='DzTjkKse')

✅ created 1 CellType record from Bionty matching ontology_id: 'CL:0000945'

❗ now recursing through parents: this only happens once, but is much slower than bulk saving

💡 you can switch this off via: lb.settings.auto_save_parents = False

💡 also saving parents of CellType(id='Z0yFV7vU', name='lymphocyte of B lineage', ontology_id='CL:0000945', description='A Lymphocyte Of B Lineage With The Commitment To Express An Immunoglobulin Complex.', updated_at=2023-09-06 17:22:37, bionty_source_id='wbOD', created_by_id='DzTjkKse')

💡 also saving parents of CellType(id='UrtDirMx', name='megakaryocyte', ontology_id='CL:0000556', synonyms='megalocaryocyte|megalokaryocyte|megacaryocyte', description='A Large Hematopoietic Cell (50 To 100 Micron) With A Lobated Nucleus. Once Mature, This Cell Undergoes Multiple Rounds Of Endomitosis And Cytoplasmic Restructuring To Allow Platelet Formation And Release.', updated_at=2023-09-06 17:22:35, bionty_source_id='wbOD', created_by_id='DzTjkKse')

✅ created 1 CellType record from Bionty matching ontology_id: 'CL:0000763'

❗ now recursing through parents: this only happens once, but is much slower than bulk saving

💡 you can switch this off via: lb.settings.auto_save_parents = False

💡 also saving parents of CellType(id='g1zY6vUW', name='myeloid cell', ontology_id='CL:0000763', description='A Cell Of The Monocyte, Granulocyte, Mast Cell, Megakaryocyte, Or Erythroid Lineage.', updated_at=2023-09-06 17:22:38, bionty_source_id='wbOD', created_by_id='DzTjkKse')

✅ created 1 CellType record from Bionty matching ontology_id: 'CL:0000988'

❗ now recursing through parents: this only happens once, but is much slower than bulk saving

💡 you can switch this off via: lb.settings.auto_save_parents = False

💡 also saving parents of CellType(id='Q0aQr5JB', name='hematopoietic cell', ontology_id='CL:0000988', synonyms='haematopoietic cell|hemopoietic cell|haemopoietic cell', description='A Cell Of A Hematopoietic Lineage.', updated_at=2023-09-06 17:22:38, bionty_source_id='wbOD', created_by_id='DzTjkKse')

✅ loaded 1 CellType record matching ontology_id: 'CL:0000548'

✅ created 1 CellType record from Bionty matching ontology_id: 'CL:0002371'

❗ now recursing through parents: this only happens once, but is much slower than bulk saving

💡 you can switch this off via: lb.settings.auto_save_parents = False

💡 also saving parents of CellType(id='QMAH6IlS', name='somatic cell', ontology_id='CL:0002371', description='A Cell Of An Organism That Does Not Pass On Its Genetic Material To The Organism'S Offspring (I.E. A Non-Germ Line Cell).', updated_at=2023-09-06 17:22:39, bionty_source_id='wbOD', created_by_id='DzTjkKse')

✅ loaded 1 CellType record matching ontology_id: 'CL:0000548'

✅ created 1 CellType record from Bionty matching ontology_id: 'CL:0000003'

❗ now recursing through parents: this only happens once, but is much slower than bulk saving

💡 you can switch this off via: lb.settings.auto_save_parents = False

💡 also saving parents of CellType(id='VT73gpK2', name='native cell', ontology_id='CL:0000003', description='A Cell That Is Found In A Natural Setting, Which Includes Multicellular Organism Cells 'In Vivo' (I.E. Part Of An Organism), And Unicellular Organisms 'In Environment' (I.E. Part Of A Natural Environment).', updated_at=2023-09-06 17:22:40, bionty_source_id='wbOD', created_by_id='DzTjkKse')

✅ created 1 CellType record from Bionty matching ontology_id: 'CL:0000000'

lb.ExperimentalFactor.validate(adata.obs.assay)
lb.Tissue.validate(adata.obs.tissue);

✅ 3 terms (100.00%) are validated for name

✅ 17 terms (100.00%) are validated for name

Because we didn’t mount a custom schema that contains a Donor registry, we use the Label registry to track donor ids:

ln.Label.validate(adata.obs.donor);

❗ received 12 unique terms, 1636 empty/duplicated terms are ignored

❗ 12 terms (100.00%) are not validated for name: D496, 621B, A29, A36, A35, 637C, A52, A37, D503, 640C, A31, 582C

Donor labels are not validated, so let’s register them:

donors = [ln.Label(name=name) for name in adata.obs.donor.unique()]
ln.save(donors)

ln.Label.validate(adata.obs.donor);

✅ 12 terms (100.00%) are validated for name

Register

modalities = ln.Modality.lookup()
experimental_factors = lb.ExperimentalFactor.lookup()
species = lb.Species.lookup()
features = ln.Feature.lookup()

Register data

When we create a File object from an AnnData, we’ll automatically link its feature sets and get information about unmapped categories:

file = ln.File.from_anndata(
    adata, description="Conde22", field=lb.Gene.ensembl_gene_id, modality=modalities.rna
)

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💡 file will be copied to default storage upon `save()` with key `None` ('.lamindb/7XqFQvVXP2vYY29SEF8V.h5ad')

💡 parsing feature names of X stored in slot 'var'

✅    36503 terms (100.00%) are validated for ensembl_gene_id

✅    linked: FeatureSet(id='Bs3qnL2mUb5A67MjKBeY', n=36503, type='number', registry='bionty.Gene', hash='dnRexHCtxtmOU81_EpoJ', modality_id='XgCPze0r', created_by_id='DzTjkKse')

💡 parsing feature names of slot 'obs'

✅    4 terms (100.00%) are validated for name

✅    linked: FeatureSet(id='u0Fwvp8ZeUa3qRFkRDSE', n=4, registry='core.Feature', hash='Lxv_RV1GMXi24AlwilEg', modality_id='8hmGOB2Q', created_by_id='DzTjkKse')

file.save()

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✅ saved 2 feature sets for slots: 'var','obs'

✅ storing file '7XqFQvVXP2vYY29SEF8V' at '.lamindb/7XqFQvVXP2vYY29SEF8V.h5ad'

The file has the following 2 linked feature sets:

file.features

Features:
  var: FeatureSet(id='Bs3qnL2mUb5A67MjKBeY', n=36503, type='number', registry='bionty.Gene', hash='dnRexHCtxtmOU81_EpoJ', updated_at=2023-09-06 17:22:44, modality_id='XgCPze0r', created_by_id='DzTjkKse')
    'RPLP1', 'SLC3A2', 'IGHV1-67', 'TLR8-AS1', 'HDGFL3', 'CA14', 'None', 'None', 'EMSY', 'URAD', ...
  obs: FeatureSet(id='u0Fwvp8ZeUa3qRFkRDSE', n=4, registry='core.Feature', hash='Lxv_RV1GMXi24AlwilEg', updated_at=2023-09-06 17:22:49, modality_id='8hmGOB2Q', created_by_id='DzTjkKse')
    donor (category)
    🔗 assay (0, bionty.ExperimentalFactor): 
    🔗 cell_type (0, bionty.CellType): 
    🔗 tissue (0, bionty.Tissue): 

Link metadata

Let us first link labels for the entire file:

file.add_labels(species.human, feature=features.species)
file.add_labels(experimental_factors.single_cell_rna_sequencing, feature=features.assay)

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✅ linked new feature 'species' together with new feature set FeatureSet(id='d3xWsVeJjHzLn6az4BQ9', n=1, registry='core.Feature', hash='bhN7SjfE3-aGE3wHK-KC', updated_at=2023-09-06 17:22:51, modality_id='8hmGOB2Q', created_by_id='DzTjkKse')

Let us know parse the columns of adata.obs for additional metadata:

cell_types = lb.CellType.from_values(adata.obs.cell_type)
efs = lb.ExperimentalFactor.from_values(adata.obs.assay)
tissues = lb.Tissue.from_values(adata.obs.tissue)
donors = ln.Label.from_values(adata.obs.donor)

file.add_labels(cell_types, feature=features.cell_type)
file.add_labels(efs, feature=features.assay)
file.add_labels(tissues, feature=features.tissue)
file.add_labels(donors, feature=features.donor)

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✅ linked feature 'donor' to registry 'core.Label'

file.features

Features:
  var: FeatureSet(id='Bs3qnL2mUb5A67MjKBeY', n=36503, type='number', registry='bionty.Gene', hash='dnRexHCtxtmOU81_EpoJ', updated_at=2023-09-06 17:22:44, modality_id='XgCPze0r', created_by_id='DzTjkKse')
    'RPLP1', 'SLC3A2', 'IGHV1-67', 'TLR8-AS1', 'HDGFL3', 'CA14', 'None', 'None', 'EMSY', 'URAD', ...
  obs: FeatureSet(id='u0Fwvp8ZeUa3qRFkRDSE', n=4, registry='core.Feature', hash='Lxv_RV1GMXi24AlwilEg', updated_at=2023-09-06 17:22:49, modality_id='8hmGOB2Q', created_by_id='DzTjkKse')
    🔗 donor (12, core.Label): 'A35', 'A31', 'A36', '582C', 'A37', 'D503', '640C', 'A29', 'D496', '637C', ...
    🔗 assay (4, bionty.ExperimentalFactor): 'single-cell RNA sequencing', '10x 3' v3', '10x 5' v2', '10x 5' v1'
    🔗 cell_type (32, bionty.CellType): 'gamma-delta T cell', 'mucosal invariant T cell', 'effector memory CD4-positive, alpha-beta T cell', 'lymphocyte', 'megakaryocyte', 'germinal center B cell', 'naive thymus-derived CD8-positive, alpha-beta T cell', 'effector memory CD8-positive, alpha-beta T cell, terminally differentiated', 'animal cell', 'CD16-positive, CD56-dim natural killer cell, human', ...
    🔗 tissue (17, bionty.Tissue): 'caecum', 'jejunal epithelium', 'lung', 'blood', 'ileum', 'duodenum', 'mesenteric lymph node', 'omentum', 'skeletal muscle tissue', 'lamina propria', ...
  external: FeatureSet(id='d3xWsVeJjHzLn6az4BQ9', n=1, registry='core.Feature', hash='bhN7SjfE3-aGE3wHK-KC', updated_at=2023-09-06 17:22:51, modality_id='8hmGOB2Q', created_by_id='DzTjkKse')
    🔗 species (1, bionty.Species): 'human'

The file is now queryable by everything we linked:

file.describe()

💡 File(id='7XqFQvVXP2vYY29SEF8V', suffix='.h5ad', accessor='AnnData', description='Conde22', size=28049505, hash='WEFcMZxJNmMiUOFrcSTaig', hash_type='md5', updated_at=2023-09-06 17:22:49)

Provenance:
  🗃️ storage: Storage(id='kSdUg2Cl', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/test-scrna', type='local', updated_at=2023-09-06 17:22:06, created_by_id='DzTjkKse')
  💫 transform: Transform(id='Nv48yAceNSh8z8', name='Validate & register scRNA-seq datasets', short_name='scrna', version='0', type=notebook, updated_at=2023-09-06 17:22:44, created_by_id='DzTjkKse')
  👣 run: Run(id='vqkf0y8uN3Qdq8ROUKih', run_at=2023-09-06 17:22:08, transform_id='Nv48yAceNSh8z8', created_by_id='DzTjkKse')
  👤 created_by: User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-09-06 17:22:06)
Features:
  var: FeatureSet(id='Bs3qnL2mUb5A67MjKBeY', n=36503, type='number', registry='bionty.Gene', hash='dnRexHCtxtmOU81_EpoJ', updated_at=2023-09-06 17:22:44, modality_id='XgCPze0r', created_by_id='DzTjkKse')
    'RPLP1', 'SLC3A2', 'IGHV1-67', 'TLR8-AS1', 'HDGFL3', 'CA14', 'None', 'None', 'EMSY', 'URAD', ...
  obs: FeatureSet(id='u0Fwvp8ZeUa3qRFkRDSE', n=4, registry='core.Feature', hash='Lxv_RV1GMXi24AlwilEg', updated_at=2023-09-06 17:22:49, modality_id='8hmGOB2Q', created_by_id='DzTjkKse')
    🔗 donor (12, core.Label): 'A35', 'A31', 'A36', '582C', 'A37', 'D503', '640C', 'A29', 'D496', '637C', ...
    🔗 assay (4, bionty.ExperimentalFactor): 'single-cell RNA sequencing', '10x 3' v3', '10x 5' v2', '10x 5' v1'
    🔗 cell_type (32, bionty.CellType): 'gamma-delta T cell', 'mucosal invariant T cell', 'effector memory CD4-positive, alpha-beta T cell', 'lymphocyte', 'megakaryocyte', 'germinal center B cell', 'naive thymus-derived CD8-positive, alpha-beta T cell', 'effector memory CD8-positive, alpha-beta T cell, terminally differentiated', 'animal cell', 'CD16-positive, CD56-dim natural killer cell, human', ...
    🔗 tissue (17, bionty.Tissue): 'caecum', 'jejunal epithelium', 'lung', 'blood', 'ileum', 'duodenum', 'mesenteric lymph node', 'omentum', 'skeletal muscle tissue', 'lamina propria', ...
  external: FeatureSet(id='d3xWsVeJjHzLn6az4BQ9', n=1, registry='core.Feature', hash='bhN7SjfE3-aGE3wHK-KC', updated_at=2023-09-06 17:22:51, modality_id='8hmGOB2Q', created_by_id='DzTjkKse')
    🔗 species (1, bionty.Species): 'human'
Labels:
  🏷️ species (1, bionty.Species): 'human'
  🏷️ tissues (17, bionty.Tissue): 'caecum', 'jejunal epithelium', 'lung', 'blood', 'ileum', 'duodenum', 'mesenteric lymph node', 'omentum', 'skeletal muscle tissue', 'lamina propria', ...
  🏷️ cell_types (32, bionty.CellType): 'gamma-delta T cell', 'mucosal invariant T cell', 'effector memory CD4-positive, alpha-beta T cell', 'lymphocyte', 'megakaryocyte', 'germinal center B cell', 'naive thymus-derived CD8-positive, alpha-beta T cell', 'effector memory CD8-positive, alpha-beta T cell, terminally differentiated', 'animal cell', 'CD16-positive, CD56-dim natural killer cell, human', ...
  🏷️ experimental_factors (4, bionty.ExperimentalFactor): 'single-cell RNA sequencing', '10x 3' v3', '10x 5' v2', '10x 5' v1'
  🏷️ labels (12, core.Label): 'A35', 'A31', 'A36', '582C', 'A37', 'D503', '640C', 'A29', 'D496', '637C', ...

A less well curated dataset

Access

Let’s now consider a dataset with less-well curated features:

pbmc68k = ln.dev.datasets.anndata_pbmc68k_reduced()
pbmc68k

AnnData object with n_obs × n_vars = 70 × 765
    obs: 'cell_type', 'n_genes', 'percent_mito', 'louvain'
    var: 'n_counts', 'highly_variable'
    uns: 'louvain', 'louvain_colors', 'neighbors', 'pca'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    obsp: 'connectivities', 'distances'

We see that this dataset is indexed by gene symbols:

pbmc68k.var.head()

	n_counts	highly_variable
index
HES4	1153.387451	True
TNFRSF4	304.358154	True
SSU72	2530.272705	False
PARK7	7451.664062	False
RBP7	272.811035	True

Validate

lb.Gene.validate(pbmc68k.var.index, lb.Gene.symbol);

✅ 695 terms (90.80%) are validated for symbol

❗ 70 terms (9.20%) are not validated for symbol: ATPIF1, C1orf228, CCBL2, RP11-782C8.1, RP11-277L2.3, RP11-156E8.1, AC079767.4, GPX1, H1FX, SELT, ATP5I, IGJ, CCDC109B, FYB, H2AFY, FAM65B, HIST1H4C, HIST1H1E, ZNRD1, C6orf48, ...

lb.Gene.inspect(pbmc68k.var.index, lb.Gene.symbol);

Show code cell output Hide code cell output

✅ 695 terms (90.80%) are validated for symbol

❗ 70 terms (9.20%) are not validated for symbol: ATPIF1, C1orf228, CCBL2, RP11-782C8.1, RP11-277L2.3, RP11-156E8.1, AC079767.4, GPX1, H1FX, SELT, ATP5I, IGJ, CCDC109B, FYB, H2AFY, FAM65B, HIST1H4C, HIST1H1E, ZNRD1, C6orf48, ...

💡    detected 54 terms with synonyms: ATPIF1, C1orf228, CCBL2, AC079767.4, H1FX, SELT, ATP5I, IGJ, CCDC109B, FYB, H2AFY, FAM65B, HIST1H4C, HIST1H1E, ZNRD1, C6orf48, SEPT7, WBSCR22, RSBN1L-AS1, CCDC132, ...

💡 →  standardize terms via .standardize()

💡    detected 5 Gene terms in Bionty for symbol: 'GPX1', 'SNORD3B-2', 'SOD2', 'RN7SL1', 'IGLL5'

💡 →  add records from Bionty to your {model_name} registry via .from_values()

💡    couldn't validate 11 terms: 'RP11-782C8.1', 'CTD-3138B18.5', 'RP11-390E23.6', 'RP11-277L2.3', 'RP11-620J15.3', 'RP11-156E8.1', 'RP3-467N11.1', 'TMBIM4-1', 'RP11-489E7.4', 'RP11-291B21.2', 'AC084018.1'

💡 →  if you are sure, create new records via ln.Gene() and save to your registry

Standardize symbols and register additional symbols from Bionty:

pbmc68k.var.index = lb.Gene.standardize(pbmc68k.var.index, lb.Gene.symbol)
gene_records = lb.Gene.from_values(pbmc68k.var.index, lb.Gene.symbol)
ln.save(gene_records)

Show code cell output Hide code cell output

💡 standardized 749/765 terms

✅ loaded 749 Gene records matching symbol: 'HES4', 'TNFRSF4', 'SSU72', 'PARK7', 'RBP7', 'SRM', 'MAD2L2', 'AGTRAP', 'TNFRSF1B', 'EFHD2', 'NECAP2', 'HP1BP3', 'C1QA', 'C1QB', 'HNRNPR', 'GALE', 'STMN1', 'CD52', 'FGR', 'ATP5IF1', ...

✅ created 5 Gene records from Bionty matching symbol: 'GPX1', 'IGLL5', 'RN7SL1', 'SNORD3B-2', 'SOD2'

❗ did not create Gene records for 11 non-validated symbols: 'AC084018.1', 'CTD-3138B18.5', 'RP11-156E8.1', 'RP11-277L2.3', 'RP11-291B21.2', 'RP11-390E23.6', 'RP11-489E7.4', 'RP11-620J15.3', 'RP11-782C8.1', 'RP3-467N11.1', 'TMBIM4-1'

In this case, we only want to register data with validated genes:

validated = lb.Gene.validate(pbmc68k.var.index, lb.Gene.symbol)

Show code cell output Hide code cell output

✅ 754 terms (98.60%) are validated for symbol

❗ 11 terms (1.40%) are not validated for symbol: RP11-782C8.1, RP11-277L2.3, RP11-156E8.1, RP3-467N11.1, RP11-390E23.6, RP11-489E7.4, RP11-291B21.2, RP11-620J15.3, TMBIM4-1, AC084018.1, CTD-3138B18.5

pbmc68k_validated = pbmc68k[:, validated].copy()

Convert gene symbols into ensembl gene ids:

records = lb.Gene.filter(id__in=[record.id for record in gene_records])
mapper = pd.DataFrame(records.values_list("symbol", "ensembl_gene_id")).set_index(0)[1]
pbmc68k_validated.var.insert(0, "gene_symbol", pbmc68k_validated.var.index)
pbmc68k_validated.var.rename(index=mapper, inplace=True)

pbmc68k_validated.var.head()

	gene_symbol	n_counts	highly_variable
ENSG00000188290	HES4	1153.387451	True
ENSG00000186827	TNFRSF4	304.358154	True
ENSG00000160075	SSU72	2530.272705	False
ENSG00000116288	PARK7	7451.664062	False
ENSG00000162444	RBP7	272.811035	True

Validate cell types:

# inspect shows none of the terms are mappable
lb.CellType.inspect(pbmc68k_validated.obs.cell_type)

# here we search the cell type names from the public ontology and grab the top match
# then add the cell type names from the pbmc68k as synonyms
celltype_bt = lb.CellType.bionty()
ontology_ids = []
mapper = {}
for ct in pbmc68k_validated.obs.cell_type.unique():
    ontology_id = celltype_bt.search(ct).iloc[0].ontology_id
    record = lb.CellType.from_bionty(ontology_id=ontology_id)
    mapper[ct] = record.name
    record.save()
    record.add_synonym(ct)

# standardize cell type names in the dataset
pbmc68k_validated.obs.cell_type = pbmc68k_validated.obs.cell_type.map(mapper)

Show code cell output Hide code cell output

❗ received 9 unique terms, 61 empty/duplicated terms are ignored

❗ 9 terms (100.00%) are not validated for name: Dendritic cells, CD19+ B, CD4+/CD45RO+ Memory, CD8+ Cytotoxic T, CD4+/CD25 T Reg, CD14+ Monocytes, CD56+ NK, CD8+/CD45RA+ Naive Cytotoxic, CD34+

💡    couldn't validate 9 terms: 'CD8+/CD45RA+ Naive Cytotoxic', 'CD14+ Monocytes', 'CD8+ Cytotoxic T', 'CD4+/CD25 T Reg', 'Dendritic cells', 'CD56+ NK', 'CD19+ B', 'CD4+/CD45RO+ Memory', 'CD34+'

💡 →  if you are sure, create new records via ln.CellType() and save to your registry

✅ created 1 CellType record from Bionty matching ontology_id: 'CL:0000451'

💡 also saving parents of CellType(id='9JGbXeUA', name='dendritic cell', ontology_id='CL:0000451', description='A Cell Of Hematopoietic Origin, Typically Resident In Particular Tissues, Specialized In The Uptake, Processing, And Transport Of Antigens To Lymph Nodes For The Purpose Of Stimulating An Immune Response Via T Cell Activation. These Cells Are Lineage Negative (Cd3-Negative, Cd19-Negative, Cd34-Negative, And Cd56-Negative).', updated_at=2023-09-06 17:23:06, bionty_source_id='wbOD', created_by_id='DzTjkKse')

✅ created 1 CellType record from Bionty matching ontology_id: 'CL:0000738'

❗ now recursing through parents: this only happens once, but is much slower than bulk saving

💡 you can switch this off via: lb.settings.auto_save_parents = False

💡 also saving parents of CellType(id='MkrH0gsX', name='leukocyte', ontology_id='CL:0000738', synonyms='white blood cell|leucocyte', description='An Achromatic Cell Of The Myeloid Or Lymphoid Lineages Capable Of Ameboid Movement, Found In Blood Or Other Tissue.', updated_at=2023-09-06 17:23:07, bionty_source_id='wbOD', created_by_id='DzTjkKse')

💡 also saving parents of CellType(id='9JGbXeUA', name='dendritic cell', ontology_id='CL:0000451', synonyms='Dendritic cells', description='A Cell Of Hematopoietic Origin, Typically Resident In Particular Tissues, Specialized In The Uptake, Processing, And Transport Of Antigens To Lymph Nodes For The Purpose Of Stimulating An Immune Response Via T Cell Activation. These Cells Are Lineage Negative (Cd3-Negative, Cd19-Negative, Cd34-Negative, And Cd56-Negative).', updated_at=2023-09-06 17:23:07, bionty_source_id='wbOD', created_by_id='DzTjkKse')

✅ created 1 CellType record from Bionty matching ontology_id: 'CL:0000907'

💡 also saving parents of CellType(id='HVKXGVqm', name='central memory CD8-positive, alpha-beta T cell', ontology_id='CL:0000907', synonyms='central CD8-positive, alpha-beta memory T-cell|central CD8-positive, alpha-beta memory T cell|central memory CD8-positive, alpha-beta T-lymphocyte|central CD8-positive, alpha-beta memory T lymphocyte|central CD8-positive, alpha-beta memory T-lymphocyte|central memory CD8-positive, alpha-beta T-cell|central memory CD8-positive, alpha-beta T lymphocyte', description='Cd8-Positive, Alpha-Beta Memory T Cell With The Phenotype Ccr7-Positive, Cd127-Positive, Cd45Ra-Negative, Cd45Ro-Positive, And Cd25-Negative.', updated_at=2023-09-06 17:23:08, bionty_source_id='wbOD', created_by_id='DzTjkKse')

💡 also saving parents of CellType(id='HVKXGVqm', name='central memory CD8-positive, alpha-beta T cell', ontology_id='CL:0000907', synonyms='central CD8-positive, alpha-beta memory T cell|central memory CD8-positive, alpha-beta T-cell|CD8+ Cytotoxic T|central memory CD8-positive, alpha-beta T lymphocyte|central CD8-positive, alpha-beta memory T-cell|central CD8-positive, alpha-beta memory T-lymphocyte|central CD8-positive, alpha-beta memory T lymphocyte|central memory CD8-positive, alpha-beta T-lymphocyte', description='Cd8-Positive, Alpha-Beta Memory T Cell With The Phenotype Ccr7-Positive, Cd127-Positive, Cd45Ra-Negative, Cd45Ro-Positive, And Cd25-Negative.', updated_at=2023-09-06 17:23:08, bionty_source_id='wbOD', created_by_id='DzTjkKse')

✅ created 1 CellType record from Bionty matching ontology_id: 'CL:0000919'

💡 also saving parents of CellType(id='ORD0dMdt', name='CD8-positive, CD25-positive, alpha-beta regulatory T cell', ontology_id='CL:0000919', synonyms='CD8+CD25+ Treg|CD8+CD25+ T-lymphocyte|CD8+CD25+ T(reg)|CD8+CD25+ T lymphocyte|CD8+CD25+ T cell|CD8-positive, CD25-positive Treg|CD8-positive, CD25-positive, alpha-beta regulatory T-lymphocyte|CD8-positive, CD25-positive, alpha-beta regulatory T-cell|CD8+CD25+ T-cell|CD8-positive, CD25-positive, alpha-beta regulatory T lymphocyte', description='A Cd8-Positive Alpha Beta-Positive T Cell With The Phenotype Foxp3-Positive And Having Suppressor Function.', updated_at=2023-09-06 17:23:08, bionty_source_id='wbOD', created_by_id='DzTjkKse')

✅ created 1 CellType record from Bionty matching ontology_id: 'CL:0000795'

❗ now recursing through parents: this only happens once, but is much slower than bulk saving

💡 you can switch this off via: lb.settings.auto_save_parents = False

💡 also saving parents of CellType(id='oTsFrhYW', name='CD8-positive, alpha-beta regulatory T cell', ontology_id='CL:0000795', synonyms='CD8-positive, alpha-beta regulatory T-cell|CD8-positive, alpha-beta Treg|CD8-positive T(reg)|CD8-positive, alpha-beta regulatory T lymphocyte|CD8+ Treg|CD8+ T(reg)|CD8+ regulatory T cell|CD8-positive, alpha-beta regulatory T-lymphocyte|CD8-positive Treg', description='A Cd8-Positive, Alpha-Beta T Cell That Regulates Overall Immune Responses As Well As The Responses Of Other T Cell Subsets Through Direct Cell-Cell Contact And Cytokine Release.', updated_at=2023-09-06 17:23:09, bionty_source_id='wbOD', created_by_id='DzTjkKse')

✅ created 1 CellType record from Bionty matching ontology_id: 'CL:0000625'

❗ now recursing through parents: this only happens once, but is much slower than bulk saving

💡 you can switch this off via: lb.settings.auto_save_parents = False

💡 also saving parents of CellType(id='VnKkQsME', name='CD8-positive, alpha-beta T cell', ontology_id='CL:0000625', synonyms='CD8-positive, alpha-beta T lymphocyte|CD8-positive, alpha-beta T-lymphocyte|CD8-positive, alpha-beta T-cell', description='A T Cell Expressing An Alpha-Beta T Cell Receptor And The Cd8 Coreceptor.', updated_at=2023-09-06 17:23:10, bionty_source_id='wbOD', created_by_id='DzTjkKse')

✅ created 1 CellType record from Bionty matching ontology_id: 'CL:0000791'

❗ now recursing through parents: this only happens once, but is much slower than bulk saving

💡 you can switch this off via: lb.settings.auto_save_parents = False

💡 also saving parents of CellType(id='WKpZjuYS', name='mature alpha-beta T cell', ontology_id='CL:0000791', synonyms='mature alpha-beta T-lymphocyte|mature alpha-beta T lymphocyte|mature alpha-beta T-cell', description='A Alpha-Beta T Cell That Has A Mature Phenotype.', updated_at=2023-09-06 17:23:11, bionty_source_id='wbOD', created_by_id='DzTjkKse')

💡 also saving parents of CellType(id='ORD0dMdt', name='CD8-positive, CD25-positive, alpha-beta regulatory T cell', ontology_id='CL:0000919', synonyms='CD8-positive, CD25-positive, alpha-beta regulatory T-cell|CD8+CD25+ T(reg)|CD8-positive, CD25-positive Treg|CD8+CD25+ T-cell|CD8-positive, CD25-positive, alpha-beta regulatory T-lymphocyte|CD8+CD25+ T lymphocyte|CD4+/CD25 T Reg|CD8+CD25+ T-lymphocyte|CD8-positive, CD25-positive, alpha-beta regulatory T lymphocyte|CD8+CD25+ Treg|CD8+CD25+ T cell', description='A Cd8-Positive Alpha Beta-Positive T Cell With The Phenotype Foxp3-Positive And Having Suppressor Function.', updated_at=2023-09-06 17:23:11, bionty_source_id='wbOD', created_by_id='DzTjkKse')

✅ created 1 CellType record from Bionty matching ontology_id: 'CL:0000576'

💡 also saving parents of CellType(id='YzV7Qgmj', name='monocyte', ontology_id='CL:0000576', description='Myeloid Mononuclear Recirculating Leukocyte That Can Act As A Precursor Of Tissue Macrophages, Osteoclasts And Some Populations Of Tissue Dendritic Cells.', updated_at=2023-09-06 17:23:11, bionty_source_id='wbOD', created_by_id='DzTjkKse')

✅ created 1 CellType record from Bionty matching ontology_id: 'CL:0000766'

❗ now recursing through parents: this only happens once, but is much slower than bulk saving

💡 you can switch this off via: lb.settings.auto_save_parents = False

💡 also saving parents of CellType(id='40onq0tm', name='myeloid leukocyte', ontology_id='CL:0000766', description='A Cell Of The Monocyte, Granulocyte, Or Mast Cell Lineage.', updated_at=2023-09-06 17:23:12, bionty_source_id='wbOD', created_by_id='DzTjkKse')

💡 also saving parents of CellType(id='YzV7Qgmj', name='monocyte', ontology_id='CL:0000576', synonyms='CD14+ Monocytes', description='Myeloid Mononuclear Recirculating Leukocyte That Can Act As A Precursor Of Tissue Macrophages, Osteoclasts And Some Populations Of Tissue Dendritic Cells.', updated_at=2023-09-06 17:23:12, bionty_source_id='wbOD', created_by_id='DzTjkKse')

✅ created 1 CellType record from Bionty matching ontology_id: 'CL:0002454'

💡 also saving parents of CellType(id='DcQ5Ivzd', name='Cd4-negative, CD8_alpha-negative, CD11b-positive dendritic cell', ontology_id='CL:0002454', synonyms='spleen double-negative dendritic cell|DC.8-4-11b+', description='Cd4-Negative, Cd8_Alpha-Negative, Cd11B-Positive Dendritic Cell Is A Conventional Dendritic Cell That Is Cd11B-Positive, Cd4-Negative, Cd8_Alpha-Negative And Is Cd205-Positive.', updated_at=2023-09-06 17:23:13, bionty_source_id='wbOD', created_by_id='DzTjkKse')

✅ created 1 CellType record from Bionty matching ontology_id: 'CL:0002465'

❗ now recursing through parents: this only happens once, but is much slower than bulk saving

💡 you can switch this off via: lb.settings.auto_save_parents = False

💡 also saving parents of CellType(id='L4SmADX0', name='CD11b-positive dendritic cell', ontology_id='CL:0002465', description='A Conventional Dendritic Cell That Expresses Cd11B (Itgam).', updated_at=2023-09-06 17:23:14, bionty_source_id='wbOD', created_by_id='DzTjkKse')

💡 also saving parents of CellType(id='DcQ5Ivzd', name='Cd4-negative, CD8_alpha-negative, CD11b-positive dendritic cell', ontology_id='CL:0002454', synonyms='spleen double-negative dendritic cell|CD8+/CD45RA+ Naive Cytotoxic|DC.8-4-11b+', description='Cd4-Negative, Cd8_Alpha-Negative, Cd11B-Positive Dendritic Cell Is A Conventional Dendritic Cell That Is Cd11B-Positive, Cd4-Negative, Cd8_Alpha-Negative And Is Cd205-Positive.', updated_at=2023-09-06 17:23:14, bionty_source_id='wbOD', created_by_id='DzTjkKse')

✅ created 1 CellType record from Bionty matching ontology_id: 'CL:0002419'

💡 also saving parents of CellType(id='2C5PhwrW', name='mature T cell', ontology_id='CL:0002419', synonyms='mature T-cell|CD3e-positive T cell', description='A T Cell That Expresses A T Cell Receptor Complex And Has Completed T Cell Selection.', updated_at=2023-09-06 17:23:15, bionty_source_id='wbOD', created_by_id='DzTjkKse')

✅ created 1 CellType record from Bionty matching ontology_id: 'CL:0000084'

❗ now recursing through parents: this only happens once, but is much slower than bulk saving

💡 you can switch this off via: lb.settings.auto_save_parents = False

💡 also saving parents of CellType(id='BxNjby0x', name='T cell', ontology_id='CL:0000084', synonyms='T-lymphocyte|T-cell|T lymphocyte', description='A Type Of Lymphocyte Whose Defining Characteristic Is The Expression Of A T Cell Receptor Complex.', updated_at=2023-09-06 17:23:15, bionty_source_id='wbOD', created_by_id='DzTjkKse')

💡 also saving parents of CellType(id='2C5PhwrW', name='mature T cell', ontology_id='CL:0002419', synonyms='CD3e-positive T cell|mature T-cell|CD34+', description='A T Cell That Expresses A T Cell Receptor Complex And Has Completed T Cell Selection.', updated_at=2023-09-06 17:23:15, bionty_source_id='wbOD', created_by_id='DzTjkKse')

Now, all cell types are validated:

lb.CellType.validate(pbmc68k_validated.obs.cell_type);

✅ 9 terms (100.00%) are validated for name

Register

file = ln.File.from_anndata(
    pbmc68k_validated,
    description="10x reference pbmc68k",
    field=lb.Gene.ensembl_gene_id,
    modality=modalities.rna,
)

💡 file will be copied to default storage upon `save()` with key `None` ('.lamindb/LqUYQ5NBOBmTw4af4ZKn.h5ad')

💡 parsing feature names of X stored in slot 'var'

✅    754 terms (100.00%) are validated for ensembl_gene_id

✅    linked: FeatureSet(id='caaugrojoACm1HppamYy', n=754, type='number', registry='bionty.Gene', hash='WMDxN7253SdzGwmznV5d', modality_id='XgCPze0r', created_by_id='DzTjkKse')

💡 parsing feature names of slot 'obs'

✅    1 term (25.00%) is validated for name

❗    3 terms (75.00%) are not validated for name: n_genes, percent_mito, louvain

✅    linked: FeatureSet(id='Nss5TL03yTdOqC7wVn4B', n=1, registry='core.Feature', hash='Q9xarVfGpJg6dU53Jh_Q', modality_id='8hmGOB2Q', created_by_id='DzTjkKse')

file.save()

Show code cell output Hide code cell output

✅ saved 2 feature sets for slots: 'var','obs'

✅ storing file 'LqUYQ5NBOBmTw4af4ZKn' at '.lamindb/LqUYQ5NBOBmTw4af4ZKn.h5ad'

cell_types = lb.CellType.from_values(pbmc68k_validated.obs.cell_type)
file.add_labels(cell_types, features.cell_type)
file.add_labels(species.human, feature=features.species)
file.add_labels(experimental_factors.single_cell_rna_sequencing, feature=features.assay)

Show code cell output Hide code cell output

✅ loaded: FeatureSet(id='d3xWsVeJjHzLn6az4BQ9', n=1, registry='core.Feature', hash='bhN7SjfE3-aGE3wHK-KC', updated_at=2023-09-06 17:22:51, modality_id='8hmGOB2Q', created_by_id='DzTjkKse')

✅ linked new feature 'species' together with new feature set FeatureSet(id='d3xWsVeJjHzLn6az4BQ9', n=1, registry='core.Feature', hash='bhN7SjfE3-aGE3wHK-KC', updated_at=2023-09-06 17:23:17, modality_id='8hmGOB2Q', created_by_id='DzTjkKse')

💡 no file links to it anymore, deleting feature set FeatureSet(id='d3xWsVeJjHzLn6az4BQ9', n=1, registry='core.Feature', hash='bhN7SjfE3-aGE3wHK-KC', updated_at=2023-09-06 17:23:17, modality_id='8hmGOB2Q', created_by_id='DzTjkKse')

✅ linked new feature 'assay' together with new feature set FeatureSet(id='ogV09OEwuyEShQdmbeGc', n=2, registry='core.Feature', hash='FWcE11dG0K-9jYgLf5d3', updated_at=2023-09-06 17:23:17, modality_id='8hmGOB2Q', created_by_id='DzTjkKse')

file.features

Features:
  var: FeatureSet(id='caaugrojoACm1HppamYy', n=754, type='number', registry='bionty.Gene', hash='WMDxN7253SdzGwmznV5d', updated_at=2023-09-06 17:23:16, modality_id='XgCPze0r', created_by_id='DzTjkKse')
    'SYPL1', 'GTF2E2', 'IFITM2', 'MRPS21', 'SLC3A2', 'FYB1', 'CD74', 'DENND2D', 'ARID4B', 'ATP6V0E1', ...
  obs: FeatureSet(id='Nss5TL03yTdOqC7wVn4B', n=1, registry='core.Feature', hash='Q9xarVfGpJg6dU53Jh_Q', updated_at=2023-09-06 17:23:17, modality_id='8hmGOB2Q', created_by_id='DzTjkKse')
    🔗 cell_type (9, bionty.CellType): 'CD16-positive, CD56-dim natural killer cell, human', 'mature T cell', 'B cell, CD19-positive', 'Cd4-negative, CD8_alpha-negative, CD11b-positive dendritic cell', 'dendritic cell', 'CD8-positive, alpha-beta memory T cell', 'central memory CD8-positive, alpha-beta T cell', 'CD8-positive, CD25-positive, alpha-beta regulatory T cell', 'monocyte'
  external: FeatureSet(id='ogV09OEwuyEShQdmbeGc', n=2, registry='core.Feature', hash='FWcE11dG0K-9jYgLf5d3', updated_at=2023-09-06 17:23:17, modality_id='8hmGOB2Q', created_by_id='DzTjkKse')
    🔗 species (1, bionty.Species): 'human'
    🔗 assay (1, bionty.ExperimentalFactor): 'single-cell RNA sequencing'

file.describe()

💡 File(id='LqUYQ5NBOBmTw4af4ZKn', suffix='.h5ad', accessor='AnnData', description='10x reference pbmc68k', size=660792, hash='GU-hbSJqGkENOxVKFLmvbA', hash_type='md5', updated_at=2023-09-06 17:23:17)

Provenance:
  🗃️ storage: Storage(id='kSdUg2Cl', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/test-scrna', type='local', updated_at=2023-09-06 17:22:06, created_by_id='DzTjkKse')
  💫 transform: Transform(id='Nv48yAceNSh8z8', name='Validate & register scRNA-seq datasets', short_name='scrna', version='0', type=notebook, updated_at=2023-09-06 17:23:16, created_by_id='DzTjkKse')
  👣 run: Run(id='vqkf0y8uN3Qdq8ROUKih', run_at=2023-09-06 17:22:08, transform_id='Nv48yAceNSh8z8', created_by_id='DzTjkKse')
  👤 created_by: User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-09-06 17:22:06)
Features:
  var: FeatureSet(id='caaugrojoACm1HppamYy', n=754, type='number', registry='bionty.Gene', hash='WMDxN7253SdzGwmznV5d', updated_at=2023-09-06 17:23:16, modality_id='XgCPze0r', created_by_id='DzTjkKse')
    'SYPL1', 'GTF2E2', 'IFITM2', 'MRPS21', 'SLC3A2', 'FYB1', 'CD74', 'DENND2D', 'ARID4B', 'ATP6V0E1', ...
  obs: FeatureSet(id='Nss5TL03yTdOqC7wVn4B', n=1, registry='core.Feature', hash='Q9xarVfGpJg6dU53Jh_Q', updated_at=2023-09-06 17:23:17, modality_id='8hmGOB2Q', created_by_id='DzTjkKse')
    🔗 cell_type (9, bionty.CellType): 'CD16-positive, CD56-dim natural killer cell, human', 'mature T cell', 'B cell, CD19-positive', 'Cd4-negative, CD8_alpha-negative, CD11b-positive dendritic cell', 'dendritic cell', 'CD8-positive, alpha-beta memory T cell', 'central memory CD8-positive, alpha-beta T cell', 'CD8-positive, CD25-positive, alpha-beta regulatory T cell', 'monocyte'
  external: FeatureSet(id='ogV09OEwuyEShQdmbeGc', n=2, registry='core.Feature', hash='FWcE11dG0K-9jYgLf5d3', updated_at=2023-09-06 17:23:17, modality_id='8hmGOB2Q', created_by_id='DzTjkKse')
    🔗 species (1, bionty.Species): 'human'
    🔗 assay (1, bionty.ExperimentalFactor): 'single-cell RNA sequencing'
Labels:
  🏷️ species (1, bionty.Species): 'human'
  🏷️ cell_types (9, bionty.CellType): 'CD16-positive, CD56-dim natural killer cell, human', 'mature T cell', 'B cell, CD19-positive', 'Cd4-negative, CD8_alpha-negative, CD11b-positive dendritic cell', 'dendritic cell', 'CD8-positive, alpha-beta memory T cell', 'central memory CD8-positive, alpha-beta T cell', 'CD8-positive, CD25-positive, alpha-beta regulatory T cell', 'monocyte'
  🏷️ experimental_factors (1, bionty.ExperimentalFactor): 'single-cell RNA sequencing'

file.view_flow()

🎉 Now let’s continue with data integration: Integrate scRNA-seq datasets