Validate & register scRNA-seq datasets

scRNA-seq measures gene expression of individual cells. It generates datasets used to define cell states associated with phenotypes.

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

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

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

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

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💡 creating schemas: core==0.46.3 bionty==0.30.2 
✅ saved: User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-08-30 13:54:05)
✅ saved: Storage(id='JcsjX8ry', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/test-scrna', type='local', updated_at=2023-08-30 13:54:05, 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

✅ loaded instance: testuser1/test-scrna (lamindb 0.51.2)

ln.track()

💡 notebook imports: lamindb==0.51.2 lnschema_bionty==0.30.2

✅ saved: Transform(id='Nv48yAceNSh8z8', name='Validate & register scRNA-seq datasets', short_name='scrna', version='0', type=notebook, updated_at=2023-08-30 13:54:07, created_by_id='DzTjkKse')

✅ saved: Run(id='CjznAQYFpOChcImqi5Rh', run_at=2023-08-30 13:54:07, transform_id='Nv48yAceNSh8z8', created_by_id='DzTjkKse')

Human immune cells: Conde22

lb.settings.species = "human"

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Transform

(Here we skip typical transformation steps that involve filtering, normalizing, and formatting.)

Let’s look at a scRNA-seq count matrix in form of an AnnData object:

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'

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 terms in Bionty for ensembl_gene_id: ENSG00000275249, ENSG00000275869, ENSG00000278817, ENSG00000277400, ENSG00000198886, ENSG00000274175, ENSG00000277856, ENSG00000271254, ENSG00000274847, ENSG00000198695, ENSG00000277475, ENSG00000228253, ENSG00000276256, ENSG00000273554, ENSG00000277630, ENSG00000198804, ENSG00000198899, ENSG00000278633, ENSG00000273748, ENSG00000275063, ...

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

💡    couldn't validate 113 terms: ENSG00000232295, ENSG00000224739, ENSG00000228139, ENSG00000272551, ENSG00000236886, ENSG00000224745, ENSG00000256045, ENSG00000236996, ENSG00000221995, ENSG00000226380, ENSG00000254561, ENSG00000239665, ENSG00000276814, ENSG00000272354, ENSG00000233776, ENSG00000278955, ENSG00000286949, ENSG00000261773, ENSG00000286228, ENSG00000273837, ...

💡 →  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')

validated = 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:

feature = ln.Feature.from_df(adata.obs.loc[:, ~validated])[0]
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:

nonval_cell_type_records = lb.CellType.from_values(
    adata.obs.cell_type[~validated], "name"
)
ln.save(nonval_cell_type_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=2023-08-30 13:54:28, bionty_source_id='ahxp', 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=2023-08-30 13:54:29, bionty_source_id='ahxp', 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=2023-08-30 13:54:29, bionty_source_id='ahxp', 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=2023-08-30 13:54:30, bionty_source_id='ahxp', 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=2023-08-30 13:54:30, bionty_source_id='ahxp', 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=2023-08-30 13:54:28, bionty_source_id='ahxp', 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=2023-08-30 13:54:31, bionty_source_id='ahxp', 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=2023-08-30 13:54:31, bionty_source_id='ahxp', 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=2023-08-30 13:54:32, bionty_source_id='ahxp', 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=2023-08-30 13:54:33, bionty_source_id='ahxp', 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

Validate external metadata

In addition to what’s already in the file, we’d like to link this file to external features including “species” and “assay”:

ln.Feature.validate("species")
ln.Feature.validate("assay");

✅ 1 term (100.00%) is validated for name

✅ 1 term (100.00%) is validated for name

Let’s search for the scRNA-seq assay label:

lb.ExperimentalFactor.search("scRNA-seq").head(2)

	id	synonyms	__ratio__
name
single-cell RNA sequencing	068T1Df6	single-cell RNA-seq|scRNA-seq|single cell RNA ...	100.000000
10x 3' v3	Vep0itYq	10X 3' v3	11.111111

scrna = lb.ExperimentalFactor.filter(id="068T1Df6").one()

Register

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", var_ref=lb.Gene.ensembl_gene_id
)

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💡 file will be copied to default storage upon `save()` with key `None` ('.lamindb/Oy6qOAHJ70JYO9KboACv.h5ad')

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

✅    36503 terms (100.00%) are validated for ensembl_gene_id

✅    linked: FeatureSet(id='Q8yofhcSJy7hGYtm9bv0', n=36503, type='float', registry='bionty.Gene', hash='dnRexHCtxtmOU81_EpoJ', created_by_id='DzTjkKse')

💡 parsing feature names of slot 'obs'

✅    4 terms (100.00%) are validated for name

✅    linked: FeatureSet(id='ux26IYb9v1nbvYgv4Ito', n=4, registry='core.Feature', hash='n6zvYbwRxENfLXtdsoga', modality_id='gpR2WiPw', created_by_id='DzTjkKse')

file.save()

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

✅ storing file 'Oy6qOAHJ70JYO9KboACv' at '.lamindb/Oy6qOAHJ70JYO9KboACv.h5ad'

The file has the following 2 linked feature sets:

file.features

'var': FeatureSet(id='Q8yofhcSJy7hGYtm9bv0', n=36503, type='float', registry='bionty.Gene', hash='dnRexHCtxtmOU81_EpoJ', updated_at=2023-08-30 13:54:36, created_by_id='DzTjkKse')
'obs': FeatureSet(id='ux26IYb9v1nbvYgv4Ito', n=4, registry='core.Feature', hash='n6zvYbwRxENfLXtdsoga', updated_at=2023-08-30 13:54:40, modality_id='gpR2WiPw', created_by_id='DzTjkKse')

You can further annotate your feature set with modality:

var_feature_set = file.features.get_feature_set("var")
modalities = ln.Modality.lookup()
var_feature_set.modality = modalities.rna
var_feature_set.save()

Link metadata

Let’s now link observational metadata by adding labels to corresponding features.

cell_types = lb.CellType.from_values(adata.obs.cell_type, field="name")
efs = lb.ExperimentalFactor.from_values(adata.obs.assay, field="name")
tissues = lb.Tissue.from_values(adata.obs.tissue, field="name")
donors = ln.Label.from_values(adata.obs["donor"])

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

✅ linked feature 'cell_type' to registry 'bionty.CellType'

✅ linked feature 'assay' to registry 'bionty.ExperimentalFactor'

✅ linked feature 'tissue' to registry 'bionty.Tissue'

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

Note that adding labels to an external feature will create an external feature set.

file.add_labels(lb.settings.species, feature="species")
file.add_labels(scrna, feature="assay")

✅ linked feature 'species' to registry 'bionty.Species'

✅ linked new feature 'species' together with new feature set FeatureSet(id='SL0xusCqyAaKaAi2wGnV', n=1, registry='core.Feature', hash='Z0L1tDnxuYxlMPKK0cqg', updated_at=2023-08-30 13:54:40, modality_id='gpR2WiPw', created_by_id='DzTjkKse')

file.features

'var': FeatureSet(id='Q8yofhcSJy7hGYtm9bv0', n=36503, type='float', registry='bionty.Gene', hash='dnRexHCtxtmOU81_EpoJ', updated_at=2023-08-30 13:54:40, modality_id='YgsyLGqP', created_by_id='DzTjkKse')
'obs': FeatureSet(id='ux26IYb9v1nbvYgv4Ito', n=4, registry='core.Feature', hash='n6zvYbwRxENfLXtdsoga', updated_at=2023-08-30 13:54:40, modality_id='gpR2WiPw', created_by_id='DzTjkKse')
'external': FeatureSet(id='SL0xusCqyAaKaAi2wGnV', n=1, registry='core.Feature', hash='Z0L1tDnxuYxlMPKK0cqg', updated_at=2023-08-30 13:54:40, modality_id='gpR2WiPw', created_by_id='DzTjkKse')

The file is now queryable by everything we linked:

file.describe()

💡 File(id='Oy6qOAHJ70JYO9KboACv', suffix='.h5ad', accessor='AnnData', description='Conde22', size=28049505, hash='WEFcMZxJNmMiUOFrcSTaig', hash_type='md5', updated_at=2023-08-30 13:54:40)

Provenance:
    🗃️ storage: Storage(id='JcsjX8ry', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/test-scrna', type='local', updated_at=2023-08-30 13:54:05, created_by_id='DzTjkKse')
    💫 transform: Transform(id='Nv48yAceNSh8z8', name='Validate & register scRNA-seq datasets', short_name='scrna', version='0', type=notebook, updated_at=2023-08-30 13:54:36, created_by_id='DzTjkKse')
    👣 run: Run(id='CjznAQYFpOChcImqi5Rh', run_at=2023-08-30 13:54:07, transform_id='Nv48yAceNSh8z8', created_by_id='DzTjkKse')
    👤 created_by: User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-08-30 13:54:05)
Features:
  var (X):
    🔗 index (36503, bionty.Gene.id): ['ejRJTjuMLw8u', 'EqxkMcG5CXg0', '0CmxX38x3U7t', 'li3IhJ0B0cGx', 'mwK1HAypNvsN'...]
  external:
    🔗 species (1, bionty.Species): ['human']
  obs (metadata):
    🔗 cell_type (32, bionty.CellType): ['naive B cell', 'macrophage', 'lymphocyte', 'naive thymus-derived CD4-positive, alpha-beta T cell', 'memory B cell']
    🔗 assay (4, bionty.ExperimentalFactor): ["10x 5' v1", "10x 5' v2", "10x 3' v3", 'single-cell RNA sequencing']
    🔗 tissue (17, bionty.Tissue): ['jejunal epithelium', 'skeletal muscle tissue', 'bone marrow', 'sigmoid colon', 'thymus']
    🔗 donor (12, core.Label): ['582C', 'D496', 'A36', 'D503', 'A52']

A less well curated dataset

Transform

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

pbcm68k = ln.dev.datasets.anndata_pbmc68k_reduced()

We see that this dataset is indexed by gene symbols:

pbcm68k.var.index

Index(['HES4', 'TNFRSF4', 'SSU72', 'PARK7', 'RBP7', 'SRM', 'MAD2L2', 'AGTRAP',
       'TNFRSF1B', 'EFHD2',
       ...
       'ATP5O', 'MRPS6', 'TTC3', 'U2AF1', 'CSTB', 'SUMO3', 'ITGB2', 'S100B',
       'PRMT2', 'MT-ND3'],
      dtype='object', name='index', length=765)

Validate

validated = lb.Gene.validate(pbcm68k.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, ...

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

pbcm68k_validated = pbcm68k[:, validated].copy()

Validate cell types:

# inspect shows none of the terms are mappable
lb.CellType.inspect(pbcm68k_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 pbcm68k as synonyms
celltype_bt = lb.CellType.bionty()
ontology_ids = []
mapper = {}
for ct in pbcm68k_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
pbcm68k_validated.obs["cell_type"] = pbcm68k_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: CD19+ B, Dendritic cells, CD4+/CD25 T Reg, CD14+ Monocytes, CD34+, CD56+ NK, CD8+/CD45RA+ Naive Cytotoxic, CD4+/CD45RO+ Memory, CD8+ Cytotoxic T

💡 →  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=2023-08-30 13:54:42, bionty_source_id='ahxp', 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=2023-08-30 13:54:42, bionty_source_id='ahxp', 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=2023-08-30 13:54:42, bionty_source_id='ahxp', created_by_id='DzTjkKse')

✅ created 1 CellType record from Bionty matching ontology_id: CL:0001087

💡 also saving parents of CellType(id='6VQXlWS7', name='effector memory CD4-positive, alpha-beta T cell, terminally differentiated', ontology_id='CL:0001087', synonyms='CD4-positive TEMRA|CD4+ TEMRA', description='A Cd4-Positive, Alpha Beta Memory T Cell With The Phenotype Cd45Ra-Positive, Cd45Ro-Negative, And Ccr7-Negative.', updated_at=2023-08-30 13:54:43, bionty_source_id='ahxp', created_by_id='DzTjkKse')

✅ created 2 CellType records from Bionty matching ontology_id: CL:4030002, CL:0000897

❗ 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='s6Ag7R5U', name='CD4-positive, alpha-beta memory T cell', ontology_id='CL:0000897', synonyms='CD4-positive, alpha-beta memory T-cell|CD4-positive, alpha-beta memory T-lymphocyte|CD4-positive, alpha-beta memory T lymphocyte', description='A Cd4-Positive, Alpha-Beta T Cell That Has Differentiated Into A Memory T Cell.', updated_at=2023-08-30 13:54:44, bionty_source_id='ahxp', created_by_id='DzTjkKse')

✅ created 1 CellType record from Bionty matching ontology_id: CL:0000624

❗ 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='05vQoepH', name='CD4-positive, alpha-beta T cell', ontology_id='CL:0000624', synonyms='CD4-positive, alpha-beta T lymphocyte|CD4-positive, alpha-beta T-cell|CD4-positive, alpha-beta T-lymphocyte', description='A Mature Alpha-Beta T Cell That Expresses An Alpha-Beta T Cell Receptor And The Cd4 Coreceptor.', updated_at=2023-08-30 13:54:44, bionty_source_id='ahxp', 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=2023-08-30 13:54:45, bionty_source_id='ahxp', created_by_id='DzTjkKse')

💡 also saving parents of CellType(id='ylUbqlrS', name='effector memory CD45RA-positive, alpha-beta T cell, terminally differentiated', ontology_id='CL:4030002', synonyms='terminally differentiated effector memory cells re-expressing CD45RA|terminally differentiated effector memory CD45RA+ T cells|TEMRA cell', description='An Alpha-Beta Memory T Cell With The Phenotype Cd45Ra-Positive.', updated_at=2023-08-30 13:54:44, bionty_source_id='ahxp', created_by_id='DzTjkKse')

💡 also saving parents of CellType(id='6VQXlWS7', name='effector memory CD4-positive, alpha-beta T cell, terminally differentiated', ontology_id='CL:0001087', synonyms='CD4-positive TEMRA|CD4+/CD45RO+ Memory|CD4+ TEMRA', description='A Cd4-Positive, Alpha Beta Memory T Cell With The Phenotype Cd45Ra-Positive, Cd45Ro-Negative, And Ccr7-Negative.', updated_at=2023-08-30 13:54:45, bionty_source_id='ahxp', created_by_id='DzTjkKse')

✅ created 1 CellType record from Bionty matching ontology_id: CL:0000910

💡 also saving parents of CellType(id='OxsmyL44', name='cytotoxic T cell', ontology_id='CL:0000910', synonyms='cytotoxic T lymphocyte|cytotoxic T-lymphocyte|cytotoxic T-cell', description='A Mature T Cell That Differentiated And Acquired Cytotoxic Function With The Phenotype Perforin-Positive And Granzyme-B Positive.', updated_at=2023-08-30 13:54:45, bionty_source_id='ahxp', created_by_id='DzTjkKse')

✅ created 1 CellType record from Bionty matching ontology_id: CL:0000911

❗ 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='yvHkIrVI', name='effector T cell', ontology_id='CL:0000911', synonyms='effector T-lymphocyte|effector T-cell|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=2023-08-30 13:54:46, bionty_source_id='ahxp', created_by_id='DzTjkKse')

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

❗ 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='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=2023-08-30 13:54:47, bionty_source_id='ahxp', 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=2023-08-30 13:54:47, bionty_source_id='ahxp', created_by_id='DzTjkKse')

💡 also saving parents of CellType(id='OxsmyL44', name='cytotoxic T cell', ontology_id='CL:0000910', synonyms='cytotoxic T lymphocyte|cytotoxic T-cell|CD8+ Cytotoxic T|cytotoxic T-lymphocyte', description='A Mature T Cell That Differentiated And Acquired Cytotoxic Function With The Phenotype Perforin-Positive And Granzyme-B Positive.', updated_at=2023-08-30 13:54:47, bionty_source_id='ahxp', 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=2023-08-30 13:54:48, bionty_source_id='ahxp', 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=2023-08-30 13:54:48, bionty_source_id='ahxp', 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=2023-08-30 13:54:49, bionty_source_id='ahxp', 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+CD25+ T lymphocyte|CD8+CD25+ T cell|CD8-positive, CD25-positive, alpha-beta regulatory T lymphocyte|CD8+CD25+ T-cell|CD8+CD25+ Treg|CD4+/CD25 T Reg|CD8+CD25+ T-lymphocyte|CD8-positive, CD25-positive, alpha-beta regulatory T-cell|CD8-positive, CD25-positive, alpha-beta regulatory T-lymphocyte|CD8+CD25+ T(reg)|CD8-positive, CD25-positive Treg', description='A Cd8-Positive Alpha Beta-Positive T Cell With The Phenotype Foxp3-Positive And Having Suppressor Function.', updated_at=2023-08-30 13:54:49, bionty_source_id='ahxp', created_by_id='DzTjkKse')

✅ created 1 CellType record from Bionty matching ontology_id: CL:0002057

💡 also saving parents of CellType(id='O0AQiAuv', name='CD14-positive, CD16-negative classical monocyte', ontology_id='CL:0002057', synonyms='CD16-negative monocyte|CD16- monocyte', description='A Classical Monocyte That Is Cd14-Positive, Cd16-Negative, Cd64-Positive, Cd163-Positive.', updated_at=2023-08-30 13:54:50, bionty_source_id='ahxp', created_by_id='DzTjkKse')

💡 also saving parents of CellType(id='O0AQiAuv', name='CD14-positive, CD16-negative classical monocyte', ontology_id='CL:0002057', synonyms='CD16-negative monocyte|CD14+ Monocytes|CD16- monocyte', description='A Classical Monocyte That Is Cd14-Positive, Cd16-Negative, Cd64-Positive, Cd163-Positive.', updated_at=2023-08-30 13:54:50, bionty_source_id='ahxp', created_by_id='DzTjkKse')

✅ created 1 CellType record from Bionty matching ontology_id: CL:0002102

💡 also saving parents of CellType(id='Xkw89opD', name='CD38-negative naive B cell', ontology_id='CL:0002102', synonyms='CD38-negative naive B lymphocyte|CD38-negative naive B-cell|CD38- naive B-cell|CD38-negative naive B-lymphocyte|CD38- naive B lymphocyte|CD38- naive B-lymphocyte|CD38- naive B cell', description='A Cd38-Negative Naive B Cell Is A Mature B Cell That Has The Phenotype Cd38-Negative, Surface Igd-Positive, Surface Igm-Positive, And Cd27-Negative, That Has Not Yet Been Activated By Antigen In The Periphery.', updated_at=2023-08-30 13:54:50, bionty_source_id='ahxp', created_by_id='DzTjkKse')

💡 also saving parents of CellType(id='Xkw89opD', name='CD38-negative naive B cell', ontology_id='CL:0002102', synonyms='CD38-negative naive B lymphocyte|CD38- naive B-cell|CD38-negative naive B-cell|CD38- naive B-lymphocyte|CD38- naive B lymphocyte|CD38-negative naive B-lymphocyte|CD8+/CD45RA+ Naive Cytotoxic|CD38- naive B cell', description='A Cd38-Negative Naive B Cell Is A Mature B Cell That Has The Phenotype Cd38-Negative, Surface Igd-Positive, Surface Igm-Positive, And Cd27-Negative, That Has Not Yet Been Activated By Antigen In The Periphery.', updated_at=2023-08-30 13:54:50, bionty_source_id='ahxp', created_by_id='DzTjkKse')

Now, all cell types are validated:

lb.CellType.validate(pbcm68k_validated.obs["cell_type"]);

✅ 9 terms (100.00%) are validated for name

Register

file = ln.File.from_anndata(
    pbcm68k_validated, description="10x reference pbmc68k", var_ref=lb.Gene.symbol
)

💡 file will be copied to default storage upon `save()` with key `None` ('.lamindb/VypFdfi6dxmi5ehxnkJO.h5ad')

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

✅    695 terms (100.00%) are validated for symbol

✅    linked: FeatureSet(id='Uz70rCpXa0cLiT3TgHPZ', n=695, type='float', registry='bionty.Gene', hash='W4ps_86b5dxk2Wd1gWTo', 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='hUA3tr2C8tX6sP83ajqT', n=1, registry='core.Feature', hash='jg4MzgvGLvYB0YL2Iuo_', modality_id='gpR2WiPw', created_by_id='DzTjkKse')

file.save()

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

✅ storing file 'VypFdfi6dxmi5ehxnkJO' at '.lamindb/VypFdfi6dxmi5ehxnkJO.h5ad'

var_feature_set = file.features.get_feature_set("var")
var_feature_set.modality = modalities.rna
var_feature_set.save()

cell_types = lb.CellType.from_values(pbcm68k_validated.obs["cell_type"], "name")
file.add_labels(cell_types, "cell_type")

file.add_labels(lb.settings.species, feature="species")
file.add_labels(scrna, feature="assay")

✅ loaded: FeatureSet(id='SL0xusCqyAaKaAi2wGnV', n=1, registry='core.Feature', hash='Z0L1tDnxuYxlMPKK0cqg', updated_at=2023-08-30 13:54:40, modality_id='gpR2WiPw', created_by_id='DzTjkKse')

✅ linked new feature 'species' together with new feature set FeatureSet(id='SL0xusCqyAaKaAi2wGnV', n=1, registry='core.Feature', hash='Z0L1tDnxuYxlMPKK0cqg', updated_at=2023-08-30 13:54:52, modality_id='gpR2WiPw', created_by_id='DzTjkKse')

💡 no file links to it anymore, deleting feature set FeatureSet(id='SL0xusCqyAaKaAi2wGnV', n=1, registry='core.Feature', hash='Z0L1tDnxuYxlMPKK0cqg', updated_at=2023-08-30 13:54:52, modality_id='gpR2WiPw', created_by_id='DzTjkKse')

✅ linked new feature 'assay' together with new feature set FeatureSet(id='yChkWBBEa9KWyx2Al02b', n=2, registry='core.Feature', hash='jYG0t-YZk1soCKbvkyJ4', updated_at=2023-08-30 13:54:52, modality_id='gpR2WiPw', created_by_id='DzTjkKse')

file.features

'var': FeatureSet(id='Uz70rCpXa0cLiT3TgHPZ', n=695, type='float', registry='bionty.Gene', hash='W4ps_86b5dxk2Wd1gWTo', updated_at=2023-08-30 13:54:51, modality_id='YgsyLGqP', created_by_id='DzTjkKse')
'obs': FeatureSet(id='hUA3tr2C8tX6sP83ajqT', n=1, registry='core.Feature', hash='jg4MzgvGLvYB0YL2Iuo_', updated_at=2023-08-30 13:54:51, modality_id='gpR2WiPw', created_by_id='DzTjkKse')
'external': FeatureSet(id='yChkWBBEa9KWyx2Al02b', n=2, registry='core.Feature', hash='jYG0t-YZk1soCKbvkyJ4', updated_at=2023-08-30 13:54:52, modality_id='gpR2WiPw', created_by_id='DzTjkKse')

file.describe()

💡 File(id='VypFdfi6dxmi5ehxnkJO', suffix='.h5ad', accessor='AnnData', description='10x reference pbmc68k', size=589484, hash='eKVXV5okt5YRYjySMTKGEw', hash_type='md5', updated_at=2023-08-30 13:54:51)

Provenance:
    🗃️ storage: Storage(id='JcsjX8ry', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/test-scrna', type='local', updated_at=2023-08-30 13:54:05, created_by_id='DzTjkKse')
    💫 transform: Transform(id='Nv48yAceNSh8z8', name='Validate & register scRNA-seq datasets', short_name='scrna', version='0', type=notebook, updated_at=2023-08-30 13:54:51, created_by_id='DzTjkKse')
    👣 run: Run(id='CjznAQYFpOChcImqi5Rh', run_at=2023-08-30 13:54:07, transform_id='Nv48yAceNSh8z8', created_by_id='DzTjkKse')
    👤 created_by: User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-08-30 13:54:05)
Features:
  var (X):
    🔗 index (695, bionty.Gene.id): ['rk0wvy6H0nfb', 'vdWoHAHsKucN', 'Jf9vlAMND2f0', 'gcWdcha1esvb', 'T3vZYmBaajgO'...]
  external:
    🔗 assay (1, bionty.ExperimentalFactor): ['single-cell RNA sequencing']
    🔗 species (1, bionty.Species): ['human']
  obs (metadata):
    🔗 cell_type (9, bionty.CellType): ['dendritic cell', 'CD14-positive, CD16-negative classical monocyte', 'CD8-positive, CD25-positive, alpha-beta regulatory T cell', 'conventional dendritic cell', 'CD38-negative naive B cell']

file.view_flow()

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