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 --modules 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('7005bwy2...') at 2025-04-15 16:37:24 UTC
! organism is ignored, define it on the dtype level
! 4 terms are not validated: 'cell_type', 'cell_type_id', 'tissue', 'disease'
→ fix typos, remove non-existent values, or save terms via .add_new_from("columns")
! 1 term is not validated: 'my new cell type'
→ fix typos, remove non-existent values, or save terms via .add_new_from("cell_type")
→ finished Run('7005bwy2') after 4s at 2025-04-15 16:37:28 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('OTYW23PP...') at 2025-04-15 16:37:30 UTC
→ notebook imports: bionty==1.3.0 lamindb==1.4.0
artifact = ln.Artifact.get(description="anndata with obs")
artifact.describe()
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Artifact .h5ad/AnnData ├── General │ ├── .uid = 'Kf015koFIBGRO6Yk0000' │ ├── .size = 46992 │ ├── .hash = 'IJORtcQUSS11QBqD-nTD0A' │ ├── .n_observations = 40 │ ├── .path = │ │ /home/runner/work/lamin-usecases/lamin-usecases/docs/analysis-flow/.lamindb/Kf015koFIBGRO6Yk0000.h5ad │ ├── .created_by = testuser1 (Test User1) │ ├── .created_at = 2025-04-15 16:37:28 │ └── .transform = 'register_example_file.py' ├── Dataset features │ ├── 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 kidney, liver, heart, brain .cell_types bionty.CellType T cell, hematopoietic stem cell, hepatoc… .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 Kf015koFIBGRO6Yk0000.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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! organism is ignored, define it on the dtype level
/opt/hostedtoolcache/Python/3.12.9/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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→ returning existing schema with same hash: Schema(uid='sjnlcZiU1opFizRLczwb', n=99, itype='bionty.Gene', is_type=False, dtype='float', hash='EZAvtYoQG37PCv6i8I5X-A', minimal_set=True, ordered_set=False, maximal_set=False, space_id=1, created_by_id=1, run_id=1, created_at=2025-04-15 16:37:28 UTC)
→ returning existing schema with same hash: Schema(uid='41ibM2K6QXd3XwUeWHWk', n=4, itype='Feature', is_type=False, otype='DataFrame', hash='wOet9S7yPSG-TfG-EPrkWw', minimal_set=True, ordered_set=False, maximal_set=False, space_id=1, created_by_id=1, run_id=1, created_at=2025-04-15 16:37:28 UTC)
Artifact .h5ad/AnnData ├── General │ ├── .uid = 'qVByuh2hmoIAyqiL0000' │ ├── .size = 38992 │ ├── .hash = 'RgGUx7ndRplZZSmalTAWiw' │ ├── .n_observations = 20 │ ├── .path = │ │ /home/runner/work/lamin-usecases/lamin-usecases/docs/analysis-flow/.lamindb/qVByuh2hmoIAyqiL0000.h5ad │ ├── .created_by = testuser1 (Test User1) │ ├── .created_at = 2025-04-15 16:37:31 │ └── .transform = 'Analysis flow' ├── Dataset features │ ├── 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='qVByuh2hmoIAyqiL0000', 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=2025-04-15 16:37:31 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 ├── 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=2025-04-15 16:37:29 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 | Kf015koFIBGRO6Yk0000 | None | anndata with obs | .h5ad | dataset | AnnData | 46992 | IJORtcQUSS11QBqD-nTD0A | None | 40 | md5 | True | False | 1 | 1 | None | None | True | 1 | 2025-04-15 16:37:28.217000+00:00 | 1 | None | 1 |
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!rm -r ./analysis-flow
!lamin delete --force analysis-flow
• deleting instance testuser1/analysis-flow