scrna1/6 Jupyter Notebook lamindata Binder

scRNA-seq#

You’ll learn how to manage a growing number of scRNA-seq data batches as a single queryable dataset.

Along the way, you’ll see how to create reports, leverage data lineage, and query statistics of individual data batches stored as files.

Specifically, you will:

  1. read a single .h5ad file as an AnnData and seed a growing dataset with it (scrna1/6, currently page)

  2. append a new data batch (a new .h5ad file) and create a new version of this dataset (scrna2/6)

  3. query & inspect files by metadata individually (scrna3/6)

  4. load the dataset into memory and save analytical results as plots (scrna4/6)

  5. iterate over the dataset, train a model, store a derived representation (scrna5/6)

  6. discuss converting a number of files to a single TileDB SOMA store of the same data (scrna6/6)

Setup#

!lamin init --storage ./test-scrna --schema bionty
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✅ saved: User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-10-04 16:38:48)

✅ saved: Storage(id='62dM2FBg', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/test-scrna', type='local', updated_at=2023-10-04 16:38:48, 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 0.55.0)

💡 notebook imports: lamindb==0.55.0 lnschema_bionty==0.31.2 pandas==1.5.3

💡 Transform(id='Nv48yAceNSh8z8', name='scRNA-seq', short_name='scrna', version='0', type=notebook, updated_at=2023-10-04 16:38:54, created_by_id='DzTjkKse')

💡 Run(id='zyBkh49M3qcVFVbvsvZ5', run_at=2023-10-04 16:38:54, transform_id='Nv48yAceNSh8z8', created_by_id='DzTjkKse')

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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"
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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
)
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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 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);

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❗ 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)

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❗ 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: 'ENSG00000276345', 'ENSG00000278704', 'ENSG00000277475', 'ENSG00000275063', 'ENSG00000228253', 'ENSG00000273748', 'ENSG00000198840', 'ENSG00000198786', 'ENSG00000277836', 'ENSG00000278817', 'ENSG00000277856', 'ENSG00000276760', 'ENSG00000277196', 'ENSG00000275869', 'ENSG00000274175', 'ENSG00000277630', 'ENSG00000274792', 'ENSG00000212907', 'ENSG00000273554', 'ENSG00000275249', ...

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

   couldn't validate 113 terms: 'ENSG00000224745', 'ENSG00000204092', 'ENSG00000237548', 'ENSG00000280374', 'ENSG00000256222', 'ENSG00000272880', 'ENSG00000272267', 'ENSG00000226403', 'ENSG00000278782', 'ENSG00000272567', 'ENSG00000254740', 'ENSG00000272196', 'ENSG00000287116', 'ENSG00000287388', 'ENSG00000244693', 'ENSG00000286601', 'ENSG00000271870', 'ENSG00000251044', 'ENSG00000272040', 'ENSG00000259820', ...

→  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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❗ 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)

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❗ 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);

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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
mHBe0XDZG0SI UBE2V1 None ENSG00000244687 7335 protein_coding ubiquitin conjugating enzyme E2 V1 [Source:HGN... UEV1A|CROC-1|CROC1|UEV-1|UBE2V uHJU Va8t 2023-10-04 16:39:07 DzTjkKse
M1WVhK0AyzOD ZNF407 None ENSG00000215421 55628 protein_coding zinc finger protein 407 [Source:HGNC Symbol;Ac... FLJ13839|FLJ20307|KIAA1703 uHJU Va8t 2023-10-04 16:39:07 DzTjkKse
bURZ02isx3bh APC2 None ENSG00000115266 10297 protein_coding APC regulator of WNT signaling pathway 2 [Sour... APCL uHJU Va8t 2023-10-04 16:39:07 DzTjkKse
Si4nW6JiaqYc None None ENSG00000272760 lncRNA novel transcript, antisense to PAXIP1 uHJU Va8t 2023-10-04 16:39:07 DzTjkKse
3vOxvz8rlbGC None None ENSG00000244998 105375790 lncRNA novel transcript, antisense to PTP4A3 uHJU Va8t 2023-10-04 16:39:07 DzTjkKse
NuEPLTdHLG1B LRP2BP-AS1 None ENSG00000250410 lncRNA LRP2BP antisense RNA 1 [Source:HGNC Symbol;Acc... RP11-714G18.1 uHJU Va8t 2023-10-04 16:39:07 DzTjkKse
Nxv80tpTncWj None None ENSG00000267576 lncRNA novel transcript, sense intronic to TSPAN16 uHJU Va8t 2023-10-04 16:39:07 DzTjkKse
5Swivl5aXmJV None None ENSG00000247193 439933 lncRNA novel transcript, antisense to CENTD1 uHJU Va8t 2023-10-04 16:39:07 DzTjkKse
fM0SLaNfcZDO None None ENSG00000250603 lncRNA novel transcript uHJU Va8t 2023-10-04 16:39:07 DzTjkKse
7M3WdE00zZqn HSPA1B None ENSG00000204388 3304 protein_coding heat shock protein family A (Hsp70) member 1B ... HSP70-2 uHJU Va8t 2023-10-04 16:39:07 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)

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❗ 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)

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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)

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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)

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❗ 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)

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

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

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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);

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❗ 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)

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

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Register #

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

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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.save()

The file has the following 2 linked feature sets:

file.features

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Features:
  var: FeatureSet(id='epYAIDvjkMEktCtQmZlq', n=36503, type='number', registry='bionty.Gene', hash='dnRexHCtxtmOU81_EpoJ', updated_at=2023-10-04 16:39:42, modality_id='8B60C3n3', created_by_id='DzTjkKse')
    'UBE2V1', 'ZNF407', 'APC2', 'None', 'None', 'LRP2BP-AS1', 'None', 'None', 'None', 'HSPA1B', 'None', 'None', 'PRKG2-AS1', 'SAR1A', 'PIPOX', 'None', 'RPS6KA4', 'None', 'MMRN1', 'ADIRF-AS1', ...
  obs: FeatureSet(id='nOcOg6PfZo1yonP1aKOL', n=4, registry='core.Feature', hash='4xEiqlhlgIHH9Nls3xEk', updated_at=2023-10-04 16:39:47, modality_id='FnQ7xHJL', 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

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Dataset(id='D1s4vH9rGh45eJ13uQiV', name='My versioned scRNA-seq dataset', version='1', hash='WEFcMZxJNmMiUOFrcSTaig', transform_id='Nv48yAceNSh8z8', run_id='zyBkh49M3qcVFVbvsvZ5', file_id='D1s4vH9rGh45eJ13uQiV', created_by_id='DzTjkKse')

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

dataset.features

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Features:
  var: FeatureSet(id='epYAIDvjkMEktCtQmZlq', n=36503, type='number', registry='bionty.Gene', hash='dnRexHCtxtmOU81_EpoJ', updated_at=2023-10-04 16:39:42, modality_id='8B60C3n3', created_by_id='DzTjkKse')
    'UBE2V1', 'ZNF407', 'APC2', 'None', 'None', 'LRP2BP-AS1', 'None', 'None', 'None', 'HSPA1B', 'None', 'None', 'PRKG2-AS1', 'SAR1A', 'PIPOX', 'None', 'RPS6KA4', 'None', 'MMRN1', 'ADIRF-AS1', ...
  obs: FeatureSet(id='nOcOg6PfZo1yonP1aKOL', n=4, registry='core.Feature', hash='4xEiqlhlgIHH9Nls3xEk', updated_at=2023-10-04 16:39:47, modality_id='FnQ7xHJL', created_by_id='DzTjkKse')
    🔗 donor (0, core.ULabel): 
    🔗 cell_type (0, bionty.CellType): 
    🔗 assay (0, bionty.ExperimentalFactor): 
    🔗 tissue (0, bionty.Tissue): 
  external: FeatureSet(id='0Pxuh64XE0XhkFXkANdp', n=1, registry='core.Feature', hash='KNKIw3BSKx44gtAsxrpX', updated_at=2023-10-04 16:39:49, modality_id='FnQ7xHJL', 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)

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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()

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Dataset(id='D1s4vH9rGh45eJ13uQiV', name='My versioned scRNA-seq dataset', version='1', hash='WEFcMZxJNmMiUOFrcSTaig', updated_at=2023-10-04 16:39:54)

Provenance:
  💫 transform: Transform(id='Nv48yAceNSh8z8', name='scRNA-seq', short_name='scrna', version='0', type=notebook, updated_at=2023-10-04 16:38:54, created_by_id='DzTjkKse')
  👣 run: Run(id='zyBkh49M3qcVFVbvsvZ5', run_at=2023-10-04 16:38:54, transform_id='Nv48yAceNSh8z8', created_by_id='DzTjkKse')
  📄 file: File(id='D1s4vH9rGh45eJ13uQiV', suffix='.h5ad', accessor='AnnData', description='Conde22', size=28049505, hash='WEFcMZxJNmMiUOFrcSTaig', hash_type='md5', updated_at=2023-10-04 16:39:54, storage_id='62dM2FBg', transform_id='Nv48yAceNSh8z8', run_id='zyBkh49M3qcVFVbvsvZ5', created_by_id='DzTjkKse')
  👤 created_by: User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-10-04 16:38:48)
Features:
  var: FeatureSet(id='epYAIDvjkMEktCtQmZlq', n=36503, type='number', registry='bionty.Gene', hash='dnRexHCtxtmOU81_EpoJ', updated_at=2023-10-04 16:39:42, modality_id='8B60C3n3', created_by_id='DzTjkKse')
    'UBE2V1', 'ZNF407', 'APC2', 'None', 'None', 'LRP2BP-AS1', 'None', 'None', 'None', 'HSPA1B', 'None', 'None', 'PRKG2-AS1', 'SAR1A', 'PIPOX', 'None', 'RPS6KA4', 'None', 'MMRN1', 'ADIRF-AS1', ...
  obs: FeatureSet(id='nOcOg6PfZo1yonP1aKOL', n=4, registry='core.Feature', hash='4xEiqlhlgIHH9Nls3xEk', updated_at=2023-10-04 16:39:47, modality_id='FnQ7xHJL', created_by_id='DzTjkKse')
    🔗 donor (12, core.ULabel): 'A52', 'A29', '621B', 'A31', '582C', 'A37', 'A36', '637C', 'D496', 'A35', ...
    🔗 cell_type (32, bionty.CellType): 'CD4-positive helper T cell', 'lymphocyte', 'mucosal invariant T cell', 'naive thymus-derived CD4-positive, alpha-beta T cell', 'germinal center B cell', 'progenitor cell', 'naive thymus-derived CD8-positive, alpha-beta T cell', 'alveolar macrophage', 'CD16-negative, CD56-bright natural killer cell, human', 'macrophage', ...
    🔗 assay (4, bionty.ExperimentalFactor): 'single-cell RNA sequencing', '10x 5' v1', '10x 3' v3', '10x 5' v2'
    🔗 tissue (17, bionty.Tissue): 'mesenteric lymph node', 'skeletal muscle tissue', 'sigmoid colon', 'duodenum', 'lamina propria', 'lung', 'omentum', 'bone marrow', 'liver', 'spleen', ...
  external: FeatureSet(id='0Pxuh64XE0XhkFXkANdp', n=1, registry='core.Feature', hash='KNKIw3BSKx44gtAsxrpX', updated_at=2023-10-04 16:39:49, modality_id='FnQ7xHJL', created_by_id='DzTjkKse')
    🔗 species (1, bionty.Species): 'human'
Labels:
  🏷️ species (1, bionty.Species): 'human'
  🏷️ tissues (17, bionty.Tissue): 'mesenteric lymph node', 'skeletal muscle tissue', 'sigmoid colon', 'duodenum', 'lamina propria', 'lung', 'omentum', 'bone marrow', 'liver', 'spleen', ...
  🏷️ cell_types (32, bionty.CellType): 'CD4-positive helper T cell', 'lymphocyte', 'mucosal invariant T cell', 'naive thymus-derived CD4-positive, alpha-beta T cell', 'germinal center B cell', 'progenitor cell', 'naive thymus-derived CD8-positive, alpha-beta T cell', 'alveolar macrophage', 'CD16-negative, CD56-bright natural killer cell, human', 'macrophage', ...
  🏷️ experimental_factors (4, bionty.ExperimentalFactor): 'single-cell RNA sequencing', '10x 5' v1', '10x 3' v3', '10x 5' v2'
  🏷️ ulabels (12, core.ULabel): 'A52', 'A29', '621B', 'A31', '582C', 'A37', 'A36', '637C', 'D496', 'A35', ...

And we can access the file like so:

dataset.file

File(id='D1s4vH9rGh45eJ13uQiV', suffix='.h5ad', accessor='AnnData', description='Conde22', size=28049505, hash='WEFcMZxJNmMiUOFrcSTaig', hash_type='md5', updated_at=2023-10-04 16:39:54, storage_id='62dM2FBg', transform_id='Nv48yAceNSh8z8', run_id='zyBkh49M3qcVFVbvsvZ5', created_by_id='DzTjkKse')

dataset.view_flow()

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