Standardize metadata on-the-fly¶
This use cases runs on a LaminDB instance with populated CellType and Pathway registries. Make sure you run the GO Ontology notebook before executing this use case.
Here, we demonstrate how to standardize the metadata on-the-fly during cell type annotation and pathway enrichment analysis using these two registries.
For more information, see:
docs:bio-registries
!lamin connect use-cases-registries
→ connected lamindb: testuser1/use-cases-registries
• to map a local dev directory, set: ln.setup.settings.dev_dir = '.'
• to map a local dev directory, call: lamin settings set dev-dir .
import lamindb as ln
import bionty as bt
from lamin_usecases import datasets as ds
import scanpy as sc
import matplotlib.pyplot as plt
import celltypist
import gseapy as gp
→ connected lamindb: testuser1/use-cases-registries
/opt/hostedtoolcache/Python/3.12.11/x64/lib/python3.12/site-packages/celltypist/classifier.py:11: FutureWarning: `__version__` is deprecated, use `importlib.metadata.version('scanpy')` instead
from scanpy import __version__ as scv
ln.track("hsPU1OENv0LS0000")
→ created Transform('hsPU1OENv0LS0000', key='analysis-registries.ipynb'), started new Run('WVGCCkUjGepzRWVh') at 2025-10-30 18:58:46 UTC
→ notebook imports: bionty==1.8.1 celltypist==1.7.1 gseapy==1.1.10 lamin_usecases==0.0.1 lamindb==1.15a1 matplotlib==3.10.7 scanpy==1.11.5
An interferon-beta treated dataset¶
A small peripheral blood mononuclear cell dataset that is split into control and stimulated groups. The stimulated group was treated with interferon beta.
Let’s load the dataset and perform some preprocessing:
adata = ds.anndata_seurat_ifnb(preprocess=False, populate_registries=True)
adata
AnnData object with n_obs × n_vars = 13999 × 9944
obs: 'stim'
var: 'symbol'
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata, n_top_genes=2000)
sc.pp.pca(adata, n_comps=20)
sc.pp.neighbors(adata, n_pcs=10)
sc.tl.umap(adata)
Analysis: cell type annotation using CellTypist¶
model = celltypist.models.Model.load(model="Immune_All_Low.pkl")
Show code cell output
🔎 No available models. Downloading...
📜 Retrieving model list from server https://celltypist.cog.sanger.ac.uk/models/models.json
📚 Total models in list: 58
📂 Storing models in /home/runner/.celltypist/data/models
💾 Downloading model [1/58]: Immune_All_Low.pkl
💾 Downloading model [2/58]: Immune_All_High.pkl
💾 Downloading model [3/58]: Adult_COVID19_PBMC.pkl
💾 Downloading model [4/58]: Adult_CynomolgusMacaque_Hippocampus.pkl
💾 Downloading model [5/58]: Adult_Human_MTG.pkl
💾 Downloading model [6/58]: Adult_Human_PancreaticIslet.pkl
💾 Downloading model [7/58]: Adult_Human_PrefrontalCortex.pkl
💾 Downloading model [8/58]: Adult_Human_Skin.pkl
💾 Downloading model [9/58]: Adult_Human_Vascular.pkl
💾 Downloading model [10/58]: Adult_Mouse_Gut.pkl
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💾 Downloading model [12/58]: Adult_Pig_Hippocampus.pkl
💾 Downloading model [13/58]: Adult_RhesusMacaque_Hippocampus.pkl
💾 Downloading model [14/58]: Adult_cHSPCs_Illumina.pkl
💾 Downloading model [15/58]: Adult_cHSPCs_Ultima.pkl
💾 Downloading model [16/58]: Autopsy_COVID19_Lung.pkl
💾 Downloading model [17/58]: COVID19_HumanChallenge_Blood.pkl
💾 Downloading model [18/58]: COVID19_Immune_Landscape.pkl
💾 Downloading model [19/58]: Cells_Adult_Breast.pkl
💾 Downloading model [20/58]: Cells_Fetal_Lung.pkl
💾 Downloading model [21/58]: Cells_Human_Tonsil.pkl
💾 Downloading model [22/58]: Cells_Intestinal_Tract.pkl
💾 Downloading model [23/58]: Cells_Lung_Airway.pkl
💾 Downloading model [24/58]: Developing_Human_Brain.pkl
💾 Downloading model [25/58]: Developing_Human_Gonads.pkl
💾 Downloading model [26/58]: Developing_Human_Hippocampus.pkl
💾 Downloading model [27/58]: Developing_Human_Organs.pkl
💾 Downloading model [28/58]: Developing_Human_Thymus.pkl
💾 Downloading model [29/58]: Developing_Mouse_Brain.pkl
💾 Downloading model [30/58]: Developing_Mouse_Hippocampus.pkl
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💾 Downloading model [32/58]: Fetal_Human_Pancreas.pkl
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💾 Downloading model [34/58]: Fetal_Human_Retina.pkl
💾 Downloading model [35/58]: Fetal_Human_Skin.pkl
💾 Downloading model [36/58]: Healthy_Adult_Heart.pkl
💾 Downloading model [37/58]: Healthy_COVID19_PBMC.pkl
💾 Downloading model [38/58]: Healthy_Human_Liver.pkl
💾 Downloading model [39/58]: Healthy_Mouse_Liver.pkl
💾 Downloading model [40/58]: Human_AdultAged_Hippocampus.pkl
💾 Downloading model [41/58]: Human_Colorectal_Cancer.pkl
💾 Downloading model [42/58]: Human_Developmental_Retina.pkl
💾 Downloading model [43/58]: Human_Embryonic_YolkSac.pkl
💾 Downloading model [44/58]: Human_Endometrium_Atlas.pkl
💾 Downloading model [45/58]: Human_IPF_Lung.pkl
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💾 Downloading model [54/58]: Mouse_Whole_Brain.pkl
💾 Downloading model [55/58]: Nuclei_Lung_Airway.pkl
💾 Downloading model [56/58]: PaediatricAdult_COVID19_Airway.pkl
💾 Downloading model [57/58]: PaediatricAdult_COVID19_PBMC.pkl
💾 Downloading model [58/58]: Pan_Fetal_Human.pkl
predictions = celltypist.annotate(
adata, model="Immune_All_Low.pkl", majority_voting=True
)
adata.obs["cell_type_celltypist"] = predictions.predicted_labels.majority_voting
🔬 Input data has 13999 cells and 9944 genes
🔗 Matching reference genes in the model
🧬 3701 features used for prediction
⚖️ Scaling input data
🖋️ Predicting labels
✅ Prediction done!
👀 Detected a neighborhood graph in the input object, will run over-clustering on the basis of it
⛓️ Over-clustering input data with resolution set to 10
🗳️ Majority voting the predictions
✅ Majority voting done!
adata.obs["cell_type_celltypist"] = bt.CellType.standardize(
adata.obs["cell_type_celltypist"]
)
sc.pl.umap(
adata,
color=["cell_type_celltypist", "stim"],
frameon=False,
legend_fontsize=10,
wspace=0.4,
)
... storing 'cell_type_celltypist' as categorical
Analysis: Pathway enrichment analysis using Enrichr¶
This analysis is based on the GSEApy scRNA-seq Example.
First, we compute differentially expressed genes using a Wilcoxon test between stimulated and control cells.
# compute differentially expressed genes
sc.tl.rank_genes_groups(
adata,
groupby="stim",
use_raw=False,
method="wilcoxon",
groups=["STIM"],
reference="CTRL",
)
rank_genes_groups_df = sc.get.rank_genes_groups_df(adata, "STIM")
rank_genes_groups_df.head()
| names | scores | logfoldchanges | pvals | pvals_adj | |
|---|---|---|---|---|---|
| 0 | ISG15 | 99.456276 | 7.132614 | 0.0 | 0.0 |
| 1 | ISG20 | 96.736786 | 5.074171 | 0.0 | 0.0 |
| 2 | IFI6 | 94.972763 | 5.828654 | 0.0 | 0.0 |
| 3 | IFIT3 | 92.482292 | 7.432296 | 0.0 | 0.0 |
| 4 | IFIT1 | 90.699104 | 8.053454 | 0.0 | 0.0 |
Next, we filter out up/down-regulated differentially expressed gene sets:
degs_up = rank_genes_groups_df[
(rank_genes_groups_df["logfoldchanges"] > 0)
& (rank_genes_groups_df["pvals_adj"] < 0.05)
]
degs_dw = rank_genes_groups_df[
(rank_genes_groups_df["logfoldchanges"] < 0)
& (rank_genes_groups_df["pvals_adj"] < 0.05)
]
degs_up.shape, degs_dw.shape
((542, 5), (935, 5))
Run pathway enrichment analysis on DEGs and plot top 10 pathways:
enr_up = gp.enrichr(degs_up.names, gene_sets="GO_Biological_Process_2023").res2d
gp.dotplot(enr_up, figsize=(2, 3), title="Up", cmap=plt.cm.autumn_r);
enr_dw = gp.enrichr(degs_dw.names, gene_sets="GO_Biological_Process_2023").res2d
gp.dotplot(enr_dw, figsize=(2, 3), title="Down", cmap=plt.cm.winter_r);
Annotate & save dataset¶
gRegister new features and labels (check out more details here):
new_features = ln.Feature.from_dataframe(adata.obs)
ln.save(new_features)
new_labels = [ln.ULabel(name=i) for i in adata.obs["stim"].unique()]
ln.save(new_labels)
! You have few permissible values for feature stim, consider dtype 'cat' instead of 'str'
! You have few permissible values for feature cell_type_celltypist, consider dtype 'cat' instead of 'str'
features = ln.Feature.lookup()
Register dataset using a Artifact object:
artifact = ln.Artifact.from_anndata(
adata,
description="seurat_ifnb_activated_Bcells",
).save()
→ writing the in-memory object into cache
# TODO: rewrite based on ln.Curator.from_anndata()
# artifact.features._add_set_from_anndata(
# var_field=bt.Gene.symbol,
# organism="human", # optionally, globally set organism via bt.settings.organism = "human"
# )
# cell_type_records = bt.CellType.from_values(adata.obs["cell_type_celltypist"])
# artifact.labels.add(cell_type_records, features.cell_type_celltypist)
# stim_records = ln.ULabel.from_values(adata.obs["stim"])
# artifact.labels.add(stim_records, features.stim)
Link pathway labels¶
Let’s enable tracking of the current notebook as the transform of this artifact:
We further create two feature sets for degs_up and degs_dw which we can later associate with the associated pathways:
degs_up_featureset = ln.Schema.from_values(
degs_up.names,
bt.Gene.symbol,
name="Up-regulated DEGs STIM vs CTRL",
organism=( # optionally, globally set organism via bt.settings.organism = "human"
"human"
),
)
degs_dw_featureset = ln.Schema.from_values(
degs_dw.names,
bt.Gene.symbol,
name="Down-regulated DEGs STIM vs CTRL",
organism=( # optionally, globally set organism via bt.settings.organism = "human"
"human"
),
)
# Link feature sets to artifact
artifact.features.add_feature_set(degs_up_featureset, slot="STIM-up-DEGs")
artifact.features.add_feature_set(degs_dw_featureset, slot="STIM-down-DEGs")
/tmp/ipykernel_3410/1859368233.py:19: DeprecationWarning: Use _add_schema instead of add_feature_set, add_feature_set will be removed in the future.
artifact.features.add_feature_set(degs_up_featureset, slot="STIM-up-DEGs")
/tmp/ipykernel_3410/1859368233.py:20: DeprecationWarning: Use _add_schema instead of add_feature_set, add_feature_set will be removed in the future.
artifact.features.add_feature_set(degs_dw_featureset, slot="STIM-down-DEGs")
Link the top 10 pathways to the corresponding differentially expressed genes:
def parse_ontology_id_from_enrichr_results(key):
"""Parse out the ontology id.
"ATF6-mediated Unfolded Protein Response (GO:0036500)" -> ("GO:0036500", "ATF6-mediated Unfolded Protein Response")
"""
id = key.split(" ")[-1].replace("(", "").replace(")", "")
name = key.replace(f" ({id})", "")
return (id, name)
# get ontology ids for the top 10 pathways
enr_up_top10 = [
pw_id[0]
for pw_id in enr_up.head(10).Term.apply(parse_ontology_id_from_enrichr_results)
]
enr_dw_top10 = [
pw_id[0]
for pw_id in enr_dw.head(10).Term.apply(parse_ontology_id_from_enrichr_results)
]
# get pathway records
enr_up_top10_pathways = bt.Pathway.from_values(enr_up_top10, bt.Pathway.ontology_id)
enr_dw_top10_pathways = bt.Pathway.from_values(enr_dw_top10, bt.Pathway.ontology_id)
Associate the pathways to the differentially expressed genes:
degs_up_featureset.pathways.set(enr_up_top10_pathways)
degs_dw_featureset.pathways.set(enr_dw_top10_pathways)
degs_up_featureset.pathways.list("name")
/tmp/ipykernel_3410/2597153897.py:4: DeprecationWarning: Use to_list instead of list, list will be removed in the future.
degs_up_featureset.pathways.list("name")
['cellular response to cytokine stimulus',
'defense response to symbiont',
'defense response to virus',
'negative regulation of viral genome replication',
'negative regulation of viral process',
'positive regulation of cytokine production',
'regulation of viral genome replication',
'response to cytokine',
'response to interferon-beta',
'response to type II interferon']
Querying pathways¶
Querying for pathways contains “interferon-beta” in the name:
bt.Pathway.filter(name__contains="interferon-beta").to_dataframe()
| uid | name | ontology_id | abbr | synonyms | description | is_locked | created_at | branch_id | space_id | created_by_id | run_id | source_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | |||||||||||||
| 4953 | 3VZq4dMe | response to interferon-beta | GO:0035456 | None | response to fiblaferon|response to fibroblast ... | Any Process That Results In A Change In State ... | False | 2025-10-30 18:58:03.163000+00:00 | 1 | 1 | 1 | None | 25 |
| 4334 | 54R2a0el | regulation of interferon-beta production | GO:0032648 | None | regulation of IFN-beta production | Any Process That Modulates The Frequency, Rate... | False | 2025-10-30 18:58:03.107000+00:00 | 1 | 1 | 1 | None | 25 |
| 3127 | 3x0xmK1y | positive regulation of interferon-beta production | GO:0032728 | None | up-regulation of interferon-beta production|up... | Any Process That Activates Or Increases The Fr... | False | 2025-10-30 18:58:03.006000+00:00 | 1 | 1 | 1 | None | 25 |
| 2130 | 1NzHDJDi | negative regulation of interferon-beta production | GO:0032688 | None | down regulation of interferon-beta production|... | Any Process That Stops, Prevents, Or Reduces T... | False | 2025-10-30 18:58:02.922000+00:00 | 1 | 1 | 1 | None | 25 |
| 684 | 1l4z0v8W | cellular response to interferon-beta | GO:0035458 | None | cellular response to fiblaferon|cellular respo... | Any Process That Results In A Change In State ... | False | 2025-10-30 18:58:02.792000+00:00 | 1 | 1 | 1 | None | 25 |
Query pathways from a gene:
bt.Pathway.filter(genes__symbol="KIR2DL1").to_dataframe()
| uid | name | ontology_id | abbr | synonyms | description | is_locked | created_at | branch_id | space_id | created_by_id | run_id | source_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | |||||||||||||
| 1346 | 7S7qlEkG | immune response-inhibiting cell surface recept... | GO:0002767 | None | immune response-inhibiting cell surface recept... | The Series Of Molecular Signals Initiated By A... | False | 2025-10-30 18:58:02.845000+00:00 | 1 | 1 | 1 | None | 25 |
Query artifacts from a pathway:
ln.Artifact.filter(feature_sets__pathways__name__icontains="interferon-beta").first()
Artifact(uid='Zg8m5rGMgQykc2230000', version=None, is_latest=True, key=None, description='seurat_ifnb_activated_Bcells', suffix='.h5ad', kind='dataset', otype='AnnData', size=215039406, hash='E-XPF_AyZafVNpL3wjsFpq', n_files=None, n_observations=13999, branch_id=1, space_id=1, storage_id=1, run_id=1, schema_id=None, created_by_id=1, created_at=2025-10-30 19:02:34 UTC, is_locked=False)
Query featuresets from a pathway to learn from which geneset this pathway was computed:
pathway = bt.Pathway.get(ontology_id="GO:0035456")
pathway
Pathway(uid='3VZq4dMe', name='response to interferon-beta', ontology_id='GO:0035456', abbr=None, synonyms='response to fiblaferon|response to fibroblast interferon|response to interferon beta', description='Any Process That Results In A Change In State Or Activity Of A Cell Or An Organism (In Terms Of Movement, Secretion, Enzyme Production, Gene Expression, Etc.) As A Result Of An Interferon-Beta Stimulus. Interferon-Beta Is A Type I Interferon.', branch_id=1, space_id=1, created_by_id=1, run_id=None, source_id=25, created_at=2025-10-30 18:58:03 UTC, is_locked=False)
degs = ln.FeatureSet.get(pathways__ontology_id=pathway.ontology_id)
Now we can get the list of genes that are differentially expressed and belong to this pathway:
contributing_genes = pathway.genes.all() & degs.genes.all()
contributing_genes.list("symbol")
/tmp/ipykernel_3410/628516556.py:2: DeprecationWarning: Use to_list instead of list, list will be removed in the future.
contributing_genes.list("symbol")
['IFI16',
'IFITM1',
'CALM1',
'IFITM3',
'XAF1',
'OAS1',
'BST2',
'IFITM2',
'PNPT1',
'IRF1',
'PLSCR1',
'MNDA',
'AIM2',
'SHFL',
'STAT1']
# clean up test instance
!lamin delete --force use-cases-registries
!rm -r ./use-cases-registries
Show code cell output
╭─ Error ──────────────────────────────────────────────────────────────────────╮
│ '/home/runner/work/lamin-usecases/lamin-usecases/docs/use-cases-registries/. │
│ lamindb' contains 1 objects - delete them prior to deleting the storage │
│ location │
╰──────────────────────────────────────────────────────────────────────────────╯