Analysis flow¶
Here, we’ll track typical data transformations like subsetting that occur during analysis.
# !pip install 'lamindb[jupyter,bionty]'
!lamin init --storage ./analysis-flow --schema bionty
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→ initialized lamindb: testuser1/analysis-flow
import lamindb as ln
import bionty as bt
→ connected lamindb: testuser1/analysis-flow
Save an initial dataset¶
register_example_file.py¶
import lamindb as ln
import bionty as bt
ln.track("K4wsS5DTYdFp0000")
# an example dataset that has a few cell type, tissue and disease annotations
adata = ln.core.datasets.anndata_with_obs()
# validate and register features
curate = ln.Curator.from_anndata(
adata,
var_index=bt.Gene.ensembl_gene_id,
categoricals={
"cell_type": bt.CellType.name,
"cell_type_id": bt.CellType.ontology_id,
"tissue": bt.Tissue.name,
"disease": bt.Disease.name,
},
organism="human",
)
curate.add_new_from("cell_type")
curate.validate()
curate.save_artifact(description="anndata with obs")
ln.finish()
!python analysis-flow-scripts/register_example_file.py
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→ connected lamindb: testuser1/analysis-flow
→ created Transform('K4wsS5DTYdFp0000'), started new Run('99QB36hR...') at 2025-01-24 14:02:42 UTC
✓ added 4 records with Feature.name for "columns": 'cell_type', 'cell_type_id', 'tissue', 'disease'
• saving validated records of 'cell_type'
✓ added 3 records from public with CellType.name for "cell_type": 'hepatocyte', 'T cell', 'hematopoietic stem cell'
✓ added 1 record with CellType.name for "cell_type": 'my new cell type'
• saving validated records of 'var_index'
✓ added 99 records from public with Gene.ensembl_gene_id for "var_index": 'ENSG00000000003', 'ENSG00000000005', 'ENSG00000000419', 'ENSG00000000457', 'ENSG00000000460', 'ENSG00000000938', 'ENSG00000000971', 'ENSG00000001036', 'ENSG00000001084', 'ENSG00000001167', 'ENSG00000001460', 'ENSG00000001461', 'ENSG00000001497', 'ENSG00000001561', 'ENSG00000001617', 'ENSG00000001626', 'ENSG00000001629', 'ENSG00000001630', 'ENSG00000001631', 'ENSG00000002016', ...
• saving validated records of 'tissue'
✓ added 4 records from public with Tissue.name for "tissue": 'brain', 'heart', 'kidney', 'liver'
• saving validated records of 'disease'
✓ added 4 records from public with Disease.name for "disease": 'chronic kidney disease', 'liver lymphoma', 'Alzheimer disease', 'cardiac ventricle disorder'
✓ "var_index" is validated against Gene.ensembl_gene_id
✓ "cell_type" is validated against CellType.name
✓ "cell_type_id" is validated against CellType.ontology_id
✓ "tissue" is validated against Tissue.name
✓ "disease" is validated against Disease.name
→ finished Run('99QB36hR') after 4s at 2025-01-24 14:02:46 UTC
Open a dataset, subset it, and register the result¶
Track the current notebook:
ln.track("eNef4Arw8nNM0000")
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→ created Transform('eNef4Arw8nNM0000'), started new Run('8TcyWE2S...') at 2025-01-24 14:02:47 UTC
→ notebook imports: bionty==1.0.0 lamindb==1.0.5
artifact = ln.Artifact.get(description="anndata with obs")
artifact.describe()
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Artifact .h5ad/AnnData ├── General │ ├── .uid = 'b3zR63lUYgctquOK0000' │ ├── .size = 46992 │ ├── .hash = 'IJORtcQUSS11QBqD-nTD0A' │ ├── .n_observations = 40 │ ├── .path = │ │ /home/runner/work/lamin-usecases/lamin-usecases/docs/analysis-flow/.lamindb/b3zR63lUYgctquOK0000.h5ad │ ├── .created_by = testuser1 (Test User1) │ ├── .created_at = 2025-01-24 14:02:46 │ └── .transform = 'register_example_file.py' ├── Dataset features/schema │ ├── var • 99 [bionty.Gene] │ │ TSPAN6 float │ │ TNMD float │ │ DPM1 float │ │ SCYL3 float │ │ FIRRM float │ │ FGR float │ │ CFH float │ │ FUCA2 float │ │ GCLC float │ │ NFYA float │ │ STPG1 float │ │ NIPAL3 float │ │ LAS1L float │ │ ENPP4 float │ │ SEMA3F float │ │ CFTR float │ │ ANKIB1 float │ │ CYP51A1 float │ │ KRIT1 float │ │ RAD52 float │ └── obs • 4 [Feature] │ cell_type cat[bionty.CellType] T cell, hematopoietic stem cell, hepatoc… │ cell_type_id cat[bionty.CellType] T cell, hematopoietic stem cell, hepatoc… │ disease cat[bionty.Disease] Alzheimer disease, cardiac ventricle dis… │ tissue cat[bionty.Tissue] brain, heart, kidney, liver └── Labels └── .tissues bionty.Tissue brain, heart, kidney, liver .cell_types bionty.CellType hepatocyte, T cell, hematopoietic stem c… .diseases bionty.Disease chronic kidney disease, liver lymphoma, …
Get a backed AnnData object¶
adata = artifact.open()
adata
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AnnDataAccessor object with n_obs × n_vars = 40 × 100
constructed for the AnnData object b3zR63lUYgctquOK0000.h5ad
obs: ['_index', 'cell_type', 'cell_type_id', 'disease', 'tissue']
var: ['_index']
Subset dataset to specific cell types and diseases¶
cell_types = artifact.cell_types.all().distinct().lookup(return_field="name")
diseases = artifact.diseases.all().distinct().lookup(return_field="name")
Create the subset:
subset_obs = adata.obs.cell_type.isin(
[cell_types.t_cell, cell_types.hematopoietic_stem_cell]
) & (adata.obs.disease.isin([diseases.liver_lymphoma, diseases.chronic_kidney_disease]))
adata_subset = adata[subset_obs]
adata_subset
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AnnDataAccessorSubset object with n_obs × n_vars = 20 × 100
obs: ['_index', 'cell_type', 'cell_type_id', 'disease', 'tissue']
var: ['_index']
adata_subset.obs[["cell_type", "disease"]].value_counts()
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cell_type disease
T cell chronic kidney disease 10
hematopoietic stem cell liver lymphoma 10
Name: count, dtype: int64
Register the subsetted AnnData:
curate = ln.Curator.from_anndata(
adata_subset.to_memory(),
var_index=bt.Gene.ensembl_gene_id,
categoricals={
"cell_type": bt.CellType.name,
"disease": bt.Disease.name,
"tissue": bt.Tissue.name,
},
organism="human",
)
curate.validate()
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✓ "var_index" is validated against Gene.ensembl_gene_id
✓ "cell_type" is validated against CellType.name
✓ "disease" is validated against Disease.name
✓ "tissue" is validated against Tissue.name
/opt/hostedtoolcache/Python/3.12.8/x64/lib/python3.12/site-packages/anndata/_core/anndata.py:1758: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.
utils.warn_names_duplicates("var")
True
artifact = curate.save_artifact(description="anndata with obs subset")
artifact.describe()
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Artifact .h5ad/AnnData ├── General │ ├── .uid = 'Ti0zq3KRRASJnRfV0000' │ ├── .size = 38992 │ ├── .hash = 'RgGUx7ndRplZZSmalTAWiw' │ ├── .n_observations = 20 │ ├── .path = │ │ /home/runner/work/lamin-usecases/lamin-usecases/docs/analysis-flow/.lamindb/Ti0zq3KRRASJnRfV0000.h5ad │ ├── .created_by = testuser1 (Test User1) │ ├── .created_at = 2025-01-24 14:02:48 │ └── .transform = 'Analysis flow' ├── Dataset features/schema │ ├── var • 99 [bionty.Gene] │ │ TSPAN6 float │ │ TNMD float │ │ DPM1 float │ │ SCYL3 float │ │ FIRRM float │ │ FGR float │ │ CFH float │ │ FUCA2 float │ │ GCLC float │ │ NFYA float │ │ STPG1 float │ │ NIPAL3 float │ │ LAS1L float │ │ ENPP4 float │ │ SEMA3F float │ │ CFTR float │ │ ANKIB1 float │ │ CYP51A1 float │ │ KRIT1 float │ │ RAD52 float │ └── obs • 4 [Feature] │ cell_type cat[bionty.CellType] T cell, hematopoietic stem cell │ disease cat[bionty.Disease] chronic kidney disease, liver lymphoma │ tissue cat[bionty.Tissue] kidney, liver │ cell_type_id cat[bionty.CellType] └── Labels └── .tissues bionty.Tissue kidney, liver .cell_types bionty.CellType T cell, hematopoietic stem cell .diseases bionty.Disease chronic kidney disease, liver lymphoma
Examine data lineage¶
Query a subsetted .h5ad artifact containing “hematopoietic stem cell” and “T cell”:
cell_types = bt.CellType.lookup()
my_subset = ln.Artifact.filter(
suffix=".h5ad",
description__endswith="subset",
cell_types__in=[
cell_types.hematopoietic_stem_cell,
cell_types.t_cell,
],
).first()
my_subset
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Artifact(uid='Ti0zq3KRRASJnRfV0000', is_latest=True, description='anndata with obs subset', suffix='.h5ad', kind='dataset', otype='AnnData', size=38992, hash='RgGUx7ndRplZZSmalTAWiw', n_observations=20, space_id=1, storage_id=1, run_id=2, created_by_id=1, created_at=2025-01-24 14:02:48 UTC)
Common questions that might arise are:
What is the history of this artifact?
Which features and labels are associated with it?
Which notebook analyzed and registered this artifact?
By whom?
And which artifact is its parent?
Let’s answer this using LaminDB:
print("--> What is the lineage of this artifact?\n")
artifact.view_lineage()
print("\n\n--> Which features and labels are associated with it?\n")
print(artifact.features)
print(artifact.labels)
print("\n\n--> Which notebook analyzed and saved this artifact\n")
print(artifact.transform)
print("\n\n--> Who save this artifact?\n")
print(artifact.created_by)
print("\n\n--> Which artifacts were inputs?\n")
display(artifact.run.input_artifacts.df())
--> What is the lineage of this artifact?
--> Which features and labels are associated with it?
Artifact .h5ad/AnnData └── Dataset features/schema ├── var • 99 [bionty.Gene] │ TSPAN6 float │ TNMD float │ DPM1 float │ SCYL3 float │ FIRRM float │ FGR float │ CFH float │ FUCA2 float │ GCLC float │ NFYA float │ STPG1 float │ NIPAL3 float │ LAS1L float │ ENPP4 float │ SEMA3F float │ CFTR float │ ANKIB1 float │ CYP51A1 float │ KRIT1 float │ RAD52 float └── obs • 4 [Feature] cell_type cat[bionty.CellType] T cell, hematopoietic stem cell disease cat[bionty.Disease] chronic kidney disease, liver lymphoma tissue cat[bionty.Tissue] kidney, liver cell_type_id cat[bionty.CellType]
Artifact .h5ad/AnnData └── Labels └── .tissues bionty.Tissue kidney, liver .cell_types bionty.CellType T cell, hematopoietic stem cell .diseases bionty.Disease chronic kidney disease, liver lymphoma
--> Which notebook analyzed and saved this artifact
Transform(uid='eNef4Arw8nNM0000', is_latest=True, key='analysis-flow.ipynb', description='Analysis flow', type='notebook', space_id=1, created_by_id=1, created_at=2025-01-24 14:02:47 UTC)
--> Who save this artifact?
User object (1)
--> Which artifacts were inputs?
| uid | key | description | suffix | kind | otype | size | hash | n_files | n_observations | _hash_type | _key_is_virtual | _overwrite_versions | space_id | storage_id | schema_id | version | is_latest | run_id | created_at | created_by_id | _aux | _branch_code | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | |||||||||||||||||||||||
| 1 | b3zR63lUYgctquOK0000 | None | anndata with obs | .h5ad | dataset | AnnData | 46992 | IJORtcQUSS11QBqD-nTD0A | None | 40 | md5 | True | False | 1 | 1 | None | None | True | 1 | 2025-01-24 14:02:46.312000+00:00 | 1 | None | 1 |
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!rm -r ./analysis-flow
!lamin delete --force analysis-flow
• deleting instance testuser1/analysis-flow