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:

• Access public biological ontologies

• Manage biological registries

• Validate, standardize & annotate

• scRNA-seq

!lamin load use-cases-registries

💡 found cached instance metadata: /home/runner/.lamin/instance--testuser1--use-cases-registries.env

💡 connected lamindb: testuser1/use-cases-registries

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

sc.settings.set_figure_params(dpi=50, facecolor="white")

ln.settings.transform.stem_uid = "hsPU1OENv0LS"
ln.settings.transform.version = "0"
ln.track()

💡 notebook imports: bionty==0.42.4 celltypist==1.6.2 gseapy==1.1.2 lamin_usecases==0.0.1 lamindb==0.69.4 matplotlib==3.8.3 scanpy==1.10.0

💡 saved: Transform(uid='hsPU1OENv0LS6K79', name='Standardize metadata on-the-fly', key='analysis-registries', version='0', type='notebook', updated_at=2024-03-31 21:37:35 UTC, created_by_id=1)

💡 saved: Run(uid='0PNBYRDUKfCH8MEq6FOC', transform_id=1, created_by_id=1)

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 × 9940
    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 Hide code cell output

2024-03-31 21:38:48,949:INFO - 🔎 No available models. Downloading...

2024-03-31 21:38:48,950:INFO - 📜 Retrieving model list from server https://celltypist.cog.sanger.ac.uk/models/models.json

2024-03-31 21:38:49,892:INFO - 📚 Total models in list: 44

2024-03-31 21:38:49,894:INFO - 📂 Storing models in /home/runner/.celltypist/data/models

2024-03-31 21:38:49,894:INFO - 💾 Downloading model [1/44]: Immune_All_Low.pkl

2024-03-31 21:38:50,908:INFO - 💾 Downloading model [2/44]: Immune_All_High.pkl

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predictions = celltypist.annotate(
    adata, model="Immune_All_Low.pkl", majority_voting=True
)
adata.obs["cell_type_celltypist"] = predictions.predicted_labels.majority_voting

2024-03-31 21:39:41,950:INFO - 🔬 Input data has 13999 cells and 9940 genes

2024-03-31 21:39:41,951:INFO - 🔗 Matching reference genes in the model

2024-03-31 21:39:43,074:INFO - 🧬 3701 features used for prediction

2024-03-31 21:39:43,078:INFO - ⚖️ Scaling input data

2024-03-31 21:39:43,552:INFO - 🖋️ Predicting labels

2024-03-31 21:39:43,740:INFO - ✅ Prediction done!

2024-03-31 21:39:43,743:INFO - 👀 Detected a neighborhood graph in the input object, will run over-clustering on the basis of it

2024-03-31 21:39:43,744:INFO - ⛓️ Over-clustering input data with resolution set to 10

2024-03-31 21:39:51,626:INFO - 🗳️ Majority voting the predictions

2024-03-31 21:39:51,680:INFO - ✅ Majority voting done!

bt.CellType.inspect(adata.obs["cell_type_celltypist"]);

❗ received 15 unique terms, 13984 empty/duplicated terms are ignored

❗ 15 terms (100.00%) are not validated for name: Intermediate macrophages, Regulatory T cells, Tcm/Naive helper T cells, Tem/Effector helper T cells, Non-classical monocytes, Tem/Trm cytotoxic T cells, CD16+ NK cells, B cells, Tcm/Naive cytotoxic T cells, NK cells, pDC, DC2, HSC/MPP, DC, Classical monocytes

   detected 2 CellType terms in Bionty as synonyms: 'DC2', 'pDC'

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

   couldn't validate 15 terms: 'DC2', 'Tcm/Naive helper T cells', 'Non-classical monocytes', 'Tem/Effector helper T cells', 'HSC/MPP', 'Tem/Trm cytotoxic T cells', 'Tcm/Naive cytotoxic T cells', 'DC', 'B cells', 'Intermediate macrophages', 'pDC', 'Classical monocytes', 'Regulatory T cells', 'NK cells', 'CD16+ NK cells'

→  if you are sure, create new records via ln.CellType() and save to your registry

adata.obs["cell_type_celltypist"] = bt.CellType.standardize(
    adata.obs["cell_type_celltypist"]
)

❗ found 2 synonyms in Bionty: ['pDC', 'DC2']
   please add corresponding CellType records via `.from_values(['plasmacytoid dendritic cell'])`

# Register cell type of found synonym
bt.CellType.from_public(name='plasmacytoid dendritic cell').save()

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

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	99.456184	7.132578	0.0	0.0
1	ISG20	96.736572	5.074123	0.0	0.0
2	IFI6	94.972672	5.828628	0.0	0.0
3	IFIT3	92.482201	7.432245	0.0	0.0
4	IFIT1	90.699089	8.053416	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), (936, 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);

Register analyzed dataset and annotate with metadata

Register new features and labels (check out more details here):

new_features = ln.Feature.from_df(adata.obs)
ln.save(new_features)
new_labels = [ln.ULabel(name=i) for i in adata.obs["stim"].unique()]
ln.save(new_labels)

features = ln.Feature.lookup()

Register dataset using a Artifact object:

artifact = ln.Artifact.from_anndata(
    adata,
    description="seurat_ifnb_activated_Bcells",
)
artifact.save()

artifact.features.add_from_anndata(
    var_field=bt.Gene.symbol,
    organism="human", # optionally, globally set organism via bt.settings.organism = "human"
)

Link cell type labels

cell_type_records = bt.CellType.from_values(adata.obs["cell_type_celltypist"])
artifact.labels.add(cell_type_records, features.cell_type_celltypist)

❗ did not create CellType records for 13 non-validated names: 'B cells', 'CD16+ NK cells', 'Classical monocytes', 'DC', 'HSC/MPP', 'Intermediate macrophages', 'NK cells', 'Non-classical monocytes', 'Regulatory T cells', 'Tcm/Naive cytotoxic T cells', 'Tcm/Naive helper T cells', 'Tem/Effector helper T cells', 'Tem/Trm cytotoxic T cells'

Link stimulation labels:

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.FeatureSet.from_values(
    degs_up.names,
    bt.Gene.symbol,
    name="Up-regulated DEGs STIM vs CTRL",
    type="category",
    organism=(  # optionally, globally set organism via bt.settings.organism = "human"
        "human"
    ),
)
degs_dw_featureset = ln.FeatureSet.from_values(
    degs_dw.names,
    bt.Gene.symbol,
    name="Down-regulated DEGs STIM vs CTRL",
    type="category",
    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")

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")

['defense response to symbiont',
 'response to type II interferon',
 'defense response to virus',
 'negative regulation of viral genome replication',
 'cellular response to cytokine stimulus',
 'positive regulation of cytokine production',
 'response to interferon-beta',
 'negative regulation of viral process',
 'response to cytokine',
 'regulation of viral genome replication']

Querying metadata

artifact.describe()

Artifact(uid='xw1jwOmx9ORdvncRUivW', suffix='.h5ad', accessor='AnnData', description='seurat_ifnb_activated_Bcells', size=215037298, hash='a0Iqvdvx_VVeNAvpdFnRW2', hash_type='sha1-fl', visibility=1, key_is_virtual=True, updated_at=2024-03-31 21:40:10 UTC)

Provenance:
  🗃️ storage: Storage(uid='M0VIrWLf', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/use-cases-registries', type='local', updated_at=2024-03-31 21:36:41 UTC, created_by_id=1)
  📔 transform: Transform(uid='hsPU1OENv0LS6K79', name='Standardize metadata on-the-fly', key='analysis-registries', version='0', type='notebook', updated_at=2024-03-31 21:37:35 UTC, created_by_id=1)
  👣 run: Run(uid='0PNBYRDUKfCH8MEq6FOC', started_at=2024-03-31 21:37:35 UTC, is_consecutive=True, transform_id=1, created_by_id=1)
  👤 created_by: User(uid='DzTjkKse', handle='testuser1', name='Test User1', updated_at=2024-03-31 21:36:41 UTC)
Features:
  var: FeatureSet(uid='EFlhjj4zJmbMK3DMSRMy', n=11283, type='number', registry='bionty.Gene', hash='nwZI5CVN-2DXJgg1K2LF', updated_at=2024-03-31 21:40:10 UTC, created_by_id=1)
    'RNF34', 'ENDOV', 'CSRP1', 'NPL', 'KAT7', 'ARHGAP6', 'BCAS2', 'COX6C', 'DRG1', 'GFOD2', 'TIGIT', 'TBC1D23', 'GBE1', 'DND1', 'CHD1L', 'MAU2', 'TMF1', 'ARHGEF7', 'SCN1B', 'BNIP1', ...
  obs: FeatureSet(uid='qqKFnGBu0DsbIUpOefda', n=2, registry='core.Feature', hash='OaUIzFo_fdikeCzKlR6S', updated_at=2024-03-31 21:40:10 UTC, created_by_id=1)
    🔗 stim (2, core.ULabel): 'STIM', 'CTRL'
    🔗 cell_type_celltypist (1, bionty.CellType): 'plasmacytoid dendritic cell'
  STIM-up-DEGs: FeatureSet(uid='uNXL0Skog4ZFx0OeF85x', name='Up-regulated DEGs STIM vs CTRL', n=661, type='category', registry='bionty.Gene', hash='A3HFmrSx-_j2sO87CWI1', updated_at=2024-03-31 21:40:11 UTC, created_by_id=1)
    'SAT1', 'CDKN2D', 'CARD16', 'KARS1', 'RPL30', 'RSAD2', 'BTG3', 'BTG3', 'EXOC3L1', 'DTX3L', 'RAB8B', 'APOBEC3F', 'PTPN2', 'TCN2', 'COX17', 'TRIM14', 'CNDP2', 'ANXA4', 'IRF9', 'IRF9', ...
  STIM-down-DEGs: FeatureSet(uid='Zy7EEM8ysXPq0vhQEiNy', name='Down-regulated DEGs STIM vs CTRL', n=1093, type='category', registry='bionty.Gene', hash='HMacGkbmO8mWTe2lCVjt', updated_at=2024-03-31 21:40:11 UTC, created_by_id=1)
    'ZFP36L1', 'NPL', 'TIMM17B', 'TIMM17B', 'CYBB', 'RPS19BP1', 'SPSB3', 'ANKH', 'HLA-DMA', 'HLA-DMA', 'HLA-DMA', 'HLA-DMA', 'HLA-DMA', 'HLA-DMA', 'HLA-DMA', 'HLA-DMA', 'FMNL1', 'SLC25A19', 'EIF3E', 'CNBP', ...
Labels:
  🏷️ cell_types (1, bionty.CellType): 'plasmacytoid dendritic cell'
  🏷️ ulabels (2, core.ULabel): 'STIM', 'CTRL'

Querying cell types

Querying for cell types contains “B cell” in the name:

bt.CellType.filter(name__contains="B cell").df().head()

	uid	name	ontology_id	abbr	synonyms	description	created_at	updated_at	public_source_id	created_by_id
id

Querying for all artifacts annotated with a cell type:

celltypes = bt.CellType.lookup()
celltypes.plasmacytoid_dendritic_cell

CellType(uid='3JO0EdVd', name='plasmacytoid dendritic cell', ontology_id='CL:0000784', synonyms='type 2 DC|pDC|interferon-producing cell|IPC|T-associated plasma cell|plasmacytoid T cell|DC2|plasmacytoid monocyte|lymphoid dendritic cell', description='A Dendritic Cell Type Of Distinct Morphology, Localization, And Surface Marker Expression (Cd123-Positive) From Other Dendritic Cell Types And Associated With Early Stage Immune Responses, Particularly The Release Of Physiologically Abundant Amounts Of Type I Interferons In Response To Infection.', updated_at=2024-03-31 21:39:52 UTC, public_source_id=21, created_by_id=1)

ln.Artifact.filter(cell_types=celltypes.plasmacytoid_dendritic_cell).df()

	uid	storage_id	key	suffix	accessor	description	version	size	hash	hash_type	n_objects	n_observations	transform_id	run_id	visibility	key_is_virtual	created_at	updated_at	created_by_id
id
1	xw1jwOmx9ORdvncRUivW	1	None	.h5ad	AnnData	seurat_ifnb_activated_Bcells	None	215037298	a0Iqvdvx_VVeNAvpdFnRW2	sha1-fl	None	None	1	1	1	True	2024-03-31 21:40:08.931332+00:00	2024-03-31 21:40:10.388231+00:00	1

Querying pathways

Querying for pathways contains “interferon-beta” in the name:

bt.Pathway.filter(name__contains="interferon-beta").df()

	uid	name	ontology_id	abbr	synonyms	description	public_source_id	created_at	updated_at	created_by_id
id
684	1l4z0v8W	cellular response to interferon-beta	GO:0035458	None	cellular response to fibroblast interferon|cel...	Any Process That Results In A Change In State ...	47	2024-03-31 21:37:02.298999+00:00	2024-03-31 21:37:02.299008+00:00	1
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...	47	2024-03-31 21:37:02.445107+00:00	2024-03-31 21:37:02.445116+00:00	1
3127	3x0xmK1y	positive regulation of interferon-beta production	GO:0032728	None	positive regulation of IFN-beta production|up-...	Any Process That Activates Or Increases The Fr...	47	2024-03-31 21:37:02.656874+00:00	2024-03-31 21:37:02.656883+00:00	1
4334	54R2a0el	regulation of interferon-beta production	GO:0032648	None	regulation of IFN-beta production	Any Process That Modulates The Frequency, Rate...	47	2024-03-31 21:37:02.780025+00:00	2024-03-31 21:37:02.780034+00:00	1
4953	3VZq4dMe	response to interferon-beta	GO:0035456	None	response to fiblaferon|response to fibroblast ...	Any Process That Results In A Change In State ...	47	2024-03-31 21:37:02.845237+00:00	2024-03-31 21:37:02.845247+00:00	1

Query pathways from a gene:

bt.Pathway.filter(genes__symbol="KIR2DL1").df()

	uid	name	ontology_id	abbr	synonyms	description	public_source_id	created_at	updated_at	created_by_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...	47	2024-03-31 21:37:02.365951+00:00	2024-03-31 21:37:02.365960+00:00	1

Query artifacts from a pathway:

ln.Artifact.filter(feature_sets__pathways__name__icontains="interferon-beta").first()

Artifact(uid='xw1jwOmx9ORdvncRUivW', suffix='.h5ad', accessor='AnnData', description='seurat_ifnb_activated_Bcells', size=215037298, hash='a0Iqvdvx_VVeNAvpdFnRW2', hash_type='sha1-fl', visibility=1, key_is_virtual=True, updated_at=2024-03-31 21:40:10 UTC, storage_id=1, transform_id=1, run_id=1, created_by_id=1)

Query featuresets from a pathway to learn from which geneset this pathway was computed:

pathway = bt.Pathway.filter(ontology_id="GO:0035456").one()
pathway

Pathway(uid='3VZq4dMe', name='response to interferon-beta', ontology_id='GO:0035456', 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.', updated_at=2024-03-31 21:37:02 UTC, public_source_id=47, created_by_id=1)

degs = ln.FeatureSet.filter(pathways__ontology_id=pathway.ontology_id).one()

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")

['STAT1',
 'BST2',
 'IFITM2',
 'AIM2',
 'XAF1',
 'CALM1',
 'SHFL',
 'PNPT1',
 'IFI16',
 'IFITM3',
 'IRF1',
 'PLSCR1',
 'MNDA',
 'OAS1',
 'IFITM1']

# clean up test instance
!lamin delete --force use-cases-registries
!rm -r ./use-cases-registries

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💡 deleting instance testuser1/use-cases-registries
❗ manually delete your stored data: /home/runner/work/lamin-usecases/lamin-usecases/docs/use-cases-registries