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scRNA-seq#

Here, we will

  1. read a single .h5ad file as an AnnData and seed a versioned dataset with it

  2. append a new data batch (a new .h5ad file) to create a new version of the dataset

  3. look at an overview of ingested files and cell markers

  4. query the data and store analytical results as plots

  5. annotate the data by a cell type prediction

  6. discuss migrating a lakehouse of files to a single TileDB SOMA store of the same data

Setup#

!lamin init --storage ./test-scrna --schema bionty
Hide code cell output 
✅ saved: User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-09-29 14:44:56)
✅ saved: Storage(id='975nKuX0', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/test-scrna', type='local', updated_at=2023-09-29 14:44:56, 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 0.54.3)

💡 notebook imports: lamindb==0.54.3 lnschema_bionty==0.31.2 pandas==1.5.3

💡 Transform(id='Nv48yAceNSh8z8', name='scRNA-seq', short_name='scrna', version='0', type=notebook, updated_at=2023-09-29 14:44:58, created_by_id='DzTjkKse')

💡 Run(id='Pd5UweAXC3cCH1aOMdr1', run_at=2023-09-29 14:44:58, transform_id='Nv48yAceNSh8z8', created_by_id='DzTjkKse')

Access #

Let us look at the data of Conde et al., Science (2022).

These data are available in standardized form from the CellxGene data portal.

Here, we’ll use it to seed a growing in-house store of scRNA-seq data managed with the corresponding metadata in LaminDB registries.

Note

If you’re not interested in managing large collections of in-house data and you’d just like to query public data, please take a look at CellxGene census, which exposes all datasets hosted in the data portal as a concatenated TileDB SOMA store.

lb.settings.species = "human"

By calling ln.dev.datasets.anndata_human_immune_cells below, we download the dataset from the CellxGene portal here and pre-populate some LaminDB registries.

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

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 is already standardized using the same public ontologies underlying lnschema-bionty, hence, we expect validation to be simple.

Nonetheless, LaminDB focuses on building clean in-house registries

Note

In the next notebook, we’ll look at the more difficult case of a non-standardized dataset that requires curation.

Validate #

Validate genes in .var#

lb.Gene.validate(adata.var.index, lb.Gene.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). Let’s inspect them to see what to do:

inspector = lb.Gene.inspect(adata.var.index, lb.Gene.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: 'ENSG00000274175', 'ENSG00000276017', 'ENSG00000198712', 'ENSG00000277196', 'ENSG00000273748', 'ENSG00000198786', 'ENSG00000198727', 'ENSG00000274792', 'ENSG00000276345', 'ENSG00000212907', 'ENSG00000277475', 'ENSG00000198804', 'ENSG00000276760', 'ENSG00000278633', 'ENSG00000198938', 'ENSG00000198886', 'ENSG00000277400', 'ENSG00000198899', 'ENSG00000198695', 'ENSG00000278704', ...

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

   couldn't validate 113 terms: 'ENSG00000273837', 'ENSG00000215271', 'ENSG00000271870', 'ENSG00000258808', 'ENSG00000270672', 'ENSG00000280710', 'ENSG00000272880', 'ENSG00000272354', 'ENSG00000263464', 'ENSG00000244952', 'ENSG00000259820', 'ENSG00000256892', 'ENSG00000254561', 'ENSG00000286228', 'ENSG00000268955', 'ENSG00000262668', 'ENSG00000272267', 'ENSG00000280095', 'ENSG00000227902', 'ENSG00000233776', ...

→  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)

❗ 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, but won’t dive into the details of converting them from an old Ensembl version to the current one.

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)

❗ 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);

Our in-house Gene registry provides rich metadata for each gene measured in the AnnData:

lb.Gene.filter().df().head(10)
symbol stable_id ensembl_gene_id ncbi_gene_ids biotype description synonyms species_id bionty_source_id updated_at created_by_id
id
10U0zkroKVT0 KRT4 None ENSG00000170477 3851 protein_coding keratin 4 [Source:HGNC Symbol;Acc:HGNC:6441] CK4|CYK4|K4 uHJU 3xbK 2023-09-29 14:45:09 DzTjkKse
K3Wk2Weq7nAK LHX5-AS1 None ENSG00000257935 lncRNA LHX5 antisense RNA 1 [Source:HGNC Symbol;Acc:H... LOCUS4010 uHJU 3xbK 2023-09-29 14:45:09 DzTjkKse
wflpKBzsK0Av OPN1LW None ENSG00000102076 5956 protein_coding opsin 1, long wave sensitive [Source:HGNC Symb... CBP|RCP|COD5|CBBM uHJU 3xbK 2023-09-29 14:45:09 DzTjkKse
w9e5Qnb9wKc6 STYX None ENSG00000198252 6815 protein_coding serine/threonine/tyrosine interacting protein ... uHJU 3xbK 2023-09-29 14:45:09 DzTjkKse
0d0GdvYbsgD6 CCDC158 None ENSG00000163749 339965 protein_coding coiled-coil domain containing 158 [Source:HGNC... FLJ25770 uHJU 3xbK 2023-09-29 14:45:09 DzTjkKse
Ye0yKQnymf0P None None ENSG00000286914 lncRNA novel transcript uHJU 3xbK 2023-09-29 14:45:09 DzTjkKse
PXHy9K7Pa0xj MKNK1-AS1 None ENSG00000269956 100507423 lncRNA MKNK1 antisense RNA 1 [Source:HGNC Symbol;Acc:... uHJU 3xbK 2023-09-29 14:45:09 DzTjkKse
lCG4pWbZk0Hc None None ENSG00000255021 lncRNA novel transcript uHJU 3xbK 2023-09-29 14:45:09 DzTjkKse
ZWC2HZdTOTdH None None ENSG00000258922 lncRNA novel transcript uHJU 3xbK 2023-09-29 14:45:09 DzTjkKse
ykfHbfg3YTFv None None ENSG00000250237 lncRNA novel transcript uHJU 3xbK 2023-09-29 14:45:09 DzTjkKse

There are about 36k genes in the registry, all for species “human”.

lb.Gene.filter().df().shape

(36503, 11)

Validate metadata in .obs#

adata.obs.columns

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

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

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

array([False,  True,  True,  True])

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

feature = ln.Feature(name="donor", type="category", registries=[ln.ULabel])
ln.save(feature)

Tip

You can also use features = ln.Feature.from_df(df) to bulk create features with types.

All metadata columns are now validated:

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

array([ True,  True,  True,  True])

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

❗ 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)

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

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

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

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

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

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
)

file.save()

The file has the following 2 linked feature sets:

file.features

Features:
  var: FeatureSet(id='EJQhGDAUMVCAW7a878Is', n=36503, type='number', registry='bionty.Gene', hash='dnRexHCtxtmOU81_EpoJ', updated_at=2023-09-29 14:45:40, modality_id='Tkw6vO00', created_by_id='DzTjkKse')
    'KRT4', 'LHX5-AS1', 'OPN1LW', 'STYX', 'CCDC158', 'None', 'None', 'MKNK1-AS1', 'None', 'None', 'LNPEP', 'LINC02485', 'None', 'None', 'IGHEP2', 'CYFIP1', 'A4GNT', 'SLC14A2', 'None', 'None', ...
  obs: FeatureSet(id='KEEZXO20pmTjLPROaTDE', n=4, registry='core.Feature', hash='NUCABLKrrAle7o2cv7hj', updated_at=2023-09-29 14:45:45, modality_id='jUAc2M1C', created_by_id='DzTjkKse')
    🔗 donor (0, core.ULabel): 
    🔗 cell_type (0, bionty.CellType): 
    🔗 assay (0, bionty.ExperimentalFactor): 
    🔗 tissue (0, bionty.Tissue):

Create a dataset from the file#

dataset = ln.Dataset(file, name="My versioned scRNA-seq dataset", version="1")

dataset

Dataset(id='nV0w72HVEfJeK6lgb7BO', name='My versioned scRNA-seq dataset', version='1', hash='WEFcMZxJNmMiUOFrcSTaig', transform_id='Nv48yAceNSh8z8', run_id='Pd5UweAXC3cCH1aOMdr1', file_id='nV0w72HVEfJeK6lgb7BO', created_by_id='DzTjkKse')

Let’s inspect the features measured in this dataset which were inherited from the file:

dataset.features

Features:
  var: FeatureSet(id='EJQhGDAUMVCAW7a878Is', n=36503, type='number', registry='bionty.Gene', hash='dnRexHCtxtmOU81_EpoJ', updated_at=2023-09-29 14:45:40, modality_id='Tkw6vO00', created_by_id='DzTjkKse')
    'KRT4', 'LHX5-AS1', 'OPN1LW', 'STYX', 'CCDC158', 'None', 'None', 'MKNK1-AS1', 'None', 'None', 'LNPEP', 'LINC02485', 'None', 'None', 'IGHEP2', 'CYFIP1', 'A4GNT', 'SLC14A2', 'None', 'None', ...
  obs: FeatureSet(id='KEEZXO20pmTjLPROaTDE', n=4, registry='core.Feature', hash='NUCABLKrrAle7o2cv7hj', updated_at=2023-09-29 14:45:45, modality_id='jUAc2M1C', created_by_id='DzTjkKse')
    🔗 donor (0, core.ULabel): 
    🔗 cell_type (0, bionty.CellType): 
    🔗 assay (0, bionty.ExperimentalFactor): 
    🔗 tissue (0, bionty.Tissue): 
  external: FeatureSet(id='wc2uklEtrF5kDstnGzpN', n=1, registry='core.Feature', hash='wXguPMg-nDtWwOHbNrZ_', updated_at=2023-09-29 14:45:47, modality_id='jUAc2M1C', created_by_id='DzTjkKse')
    🔗 species (0, bionty.Species):

This looks all good, hence, let’s save it:

dataset.save()

Annotate by linking labels:

dataset.labels.add(experimental_factors.single_cell_rna_sequencing, features.assay)
dataset.labels.add(species.human, features.species)
dataset.labels.add(adata.obs.cell_type, feature=features.cell_type)
dataset.labels.add(adata.obs.assay, feature=features.assay)
dataset.labels.add(adata.obs.tissue, feature=features.tissue)
dataset.labels.add(adata.obs.donor, feature=features.donor)

For this version 1 of the dataset, dataset and file match each other. But they’re independently tracked and queryable through their registries.

dataset.describe()

Dataset(id='nV0w72HVEfJeK6lgb7BO', name='My versioned scRNA-seq dataset', version='1', hash='WEFcMZxJNmMiUOFrcSTaig', updated_at=2023-09-29 14:45:51)

Provenance:
  💫 transform: Transform(id='Nv48yAceNSh8z8', name='scRNA-seq', short_name='scrna', version='0', type=notebook, updated_at=2023-09-29 14:45:51, created_by_id='DzTjkKse')
  👣 run: Run(id='Pd5UweAXC3cCH1aOMdr1', run_at=2023-09-29 14:44:58, transform_id='Nv48yAceNSh8z8', created_by_id='DzTjkKse')
  📄 file: File(id='nV0w72HVEfJeK6lgb7BO', suffix='.h5ad', accessor='AnnData', description='Conde22', size=28049505, hash='WEFcMZxJNmMiUOFrcSTaig', hash_type='md5', updated_at=2023-09-29 14:45:51, storage_id='975nKuX0', transform_id='Nv48yAceNSh8z8', run_id='Pd5UweAXC3cCH1aOMdr1', created_by_id='DzTjkKse')
  👤 created_by: User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-09-29 14:44:56)
Features:
  var: FeatureSet(id='EJQhGDAUMVCAW7a878Is', n=36503, type='number', registry='bionty.Gene', hash='dnRexHCtxtmOU81_EpoJ', updated_at=2023-09-29 14:45:40, modality_id='Tkw6vO00', created_by_id='DzTjkKse')
    'KRT4', 'LHX5-AS1', 'OPN1LW', 'STYX', 'CCDC158', 'None', 'None', 'MKNK1-AS1', 'None', 'None', 'LNPEP', 'LINC02485', 'None', 'None', 'IGHEP2', 'CYFIP1', 'A4GNT', 'SLC14A2', 'None', 'None', ...
  obs: FeatureSet(id='KEEZXO20pmTjLPROaTDE', n=4, registry='core.Feature', hash='NUCABLKrrAle7o2cv7hj', updated_at=2023-09-29 14:45:45, modality_id='jUAc2M1C', created_by_id='DzTjkKse')
    🔗 donor (12, core.ULabel): '621B', '640C', 'D496', 'A52', 'A36', '582C', 'A29', 'D503', 'A37', 'A31', ...
    🔗 cell_type (32, bionty.CellType): 'progenitor cell', 'T follicular helper cell', 'naive thymus-derived CD8-positive, alpha-beta T cell', 'dendritic cell, human', 'CD8-positive, alpha-beta memory T cell, CD45RO-positive', 'classical monocyte', 'regulatory T cell', 'group 3 innate lymphoid cell', 'CD4-positive helper T cell', 'non-classical monocyte', ...
    🔗 assay (4, bionty.ExperimentalFactor): 'single-cell RNA sequencing', '10x 5' v2', '10x 3' v3', '10x 5' v1'
    🔗 tissue (17, bionty.Tissue): 'mesenteric lymph node', 'lung', 'sigmoid colon', 'thymus', 'lamina propria', 'bone marrow', 'ileum', 'jejunal epithelium', 'spleen', 'thoracic lymph node', ...
  external: FeatureSet(id='wc2uklEtrF5kDstnGzpN', n=1, registry='core.Feature', hash='wXguPMg-nDtWwOHbNrZ_', updated_at=2023-09-29 14:45:47, modality_id='jUAc2M1C', created_by_id='DzTjkKse')
    🔗 species (1, bionty.Species): 'human'
Labels:
  🏷️ species (1, bionty.Species): 'human'
  🏷️ tissues (17, bionty.Tissue): 'mesenteric lymph node', 'lung', 'sigmoid colon', 'thymus', 'lamina propria', 'bone marrow', 'ileum', 'jejunal epithelium', 'spleen', 'thoracic lymph node', ...
  🏷️ cell_types (32, bionty.CellType): 'progenitor cell', 'T follicular helper cell', 'naive thymus-derived CD8-positive, alpha-beta T cell', 'dendritic cell, human', 'CD8-positive, alpha-beta memory T cell, CD45RO-positive', 'classical monocyte', 'regulatory T cell', 'group 3 innate lymphoid cell', 'CD4-positive helper T cell', 'non-classical monocyte', ...
  🏷️ experimental_factors (4, bionty.ExperimentalFactor): 'single-cell RNA sequencing', '10x 5' v2', '10x 3' v3', '10x 5' v1'
  🏷️ ulabels (12, core.ULabel): '621B', '640C', 'D496', 'A52', 'A36', '582C', 'A29', 'D503', 'A37', 'A31', ...

And we can access the file like so:

dataset.file

File(id='nV0w72HVEfJeK6lgb7BO', suffix='.h5ad', accessor='AnnData', description='Conde22', size=28049505, hash='WEFcMZxJNmMiUOFrcSTaig', hash_type='md5', updated_at=2023-09-29 14:45:51, storage_id='975nKuX0', transform_id='Nv48yAceNSh8z8', run_id='Pd5UweAXC3cCH1aOMdr1', created_by_id='DzTjkKse')

dataset.view_flow()
https://d33wubrfki0l68.cloudfront.net/8301084907f15447545ea09682d40246ebe75f0c/ada46/_images/2608347264827e32729d4b0db7761128fbbf28061e2bcc7d44bc7fb50897fa00.svg