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:

!lamin load use-cases-registries
πŸ’‘ found cached instance metadata: /home/runner/.lamin/instance--testuser1--use-cases-registries.env
πŸ’‘ loaded instance: 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
πŸ’‘ lamindb instance: testuser1/use-cases-registries
sc.settings.set_figure_params(dpi=50, facecolor="white")
ln.transform.stem_uid = "hsPU1OENv0LS"
ln.transform.version = "0"
ln.track()
πŸ’‘ notebook imports: bionty==0.41.0 celltypist==1.6.2 gseapy==1.1.2 lamin_usecases==0.0.1 lamindb==0.68.0 matplotlib==3.8.3 scanpy==1.9.8
πŸ’‘ saved: Transform(uid='hsPU1OENv0LS6K79', name='Standardize metadata on-the-fly', short_name='analysis-registries', version='0', type=notebook, updated_at=2024-03-04 17:06:58 UTC, created_by_id=1)
πŸ’‘ saved: Run(uid='FcRMckoaPABllIB4Y4wN', run_at=2024-03-04 17:06:58 UTC, 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 Γ— n_vars = 13999 Γ— 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")
Hide code cell output
2024-03-04 17:07:55,564:INFO - πŸ”Ž No available models. Downloading...
2024-03-04 17:07:55,565:INFO - πŸ“œ Retrieving model list from server https://celltypist.cog.sanger.ac.uk/models/models.json
2024-03-04 17:08:05,104:INFO - πŸ“š Total models in list: 44
2024-03-04 17:08:05,106:INFO - πŸ“‚ Storing models in /home/runner/.celltypist/data/models
2024-03-04 17:08:05,107:INFO - πŸ’Ύ Downloading model [1/44]: Immune_All_Low.pkl
2024-03-04 17:08:11,019:INFO - πŸ’Ύ Downloading model [2/44]: Immune_All_High.pkl
2024-03-04 17:08:12,540:INFO - πŸ’Ύ Downloading model [3/44]: Adult_CynomolgusMacaque_Hippocampus.pkl
2024-03-04 17:08:18,643:INFO - πŸ’Ύ Downloading model [4/44]: Adult_Human_PancreaticIslet.pkl
2024-03-04 17:08:19,712:INFO - πŸ’Ύ Downloading model [5/44]: Adult_Human_Skin.pkl
2024-03-04 17:08:25,425:INFO - πŸ’Ύ Downloading model [6/44]: Adult_Mouse_Gut.pkl
2024-03-04 17:08:27,159:INFO - πŸ’Ύ Downloading model [7/44]: Adult_Mouse_OlfactoryBulb.pkl
2024-03-04 17:08:28,527:INFO - πŸ’Ύ Downloading model [8/44]: Adult_Pig_Hippocampus.pkl
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2024-03-04 17:08:35,460:INFO - πŸ’Ύ Downloading model [10/44]: Autopsy_COVID19_Lung.pkl
2024-03-04 17:08:36,682:INFO - πŸ’Ύ Downloading model [11/44]: COVID19_HumanChallenge_Blood.pkl
2024-03-04 17:08:42,803:INFO - πŸ’Ύ Downloading model [12/44]: COVID19_Immune_Landscape.pkl
2024-03-04 17:08:44,329:INFO - πŸ’Ύ Downloading model [13/44]: Cells_Fetal_Lung.pkl
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2024-03-04 17:08:49,472:INFO - πŸ’Ύ Downloading model [16/44]: Developing_Human_Brain.pkl
2024-03-04 17:08:50,701:INFO - πŸ’Ύ Downloading model [17/44]: Developing_Human_Gonads.pkl
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2024-03-04 17:08:58,157:INFO - πŸ’Ύ Downloading model [19/44]: Developing_Human_Organs.pkl
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2024-03-04 17:09:00,902:INFO - πŸ’Ύ Downloading model [21/44]: Developing_Mouse_Brain.pkl
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2024-03-04 17:09:03,710:INFO - πŸ’Ύ Downloading model [23/44]: Fetal_Human_AdrenalGlands.pkl
2024-03-04 17:09:04,786:INFO - πŸ’Ύ Downloading model [24/44]: Fetal_Human_Pancreas.pkl
2024-03-04 17:09:06,159:INFO - πŸ’Ύ Downloading model [25/44]: Fetal_Human_Pituitary.pkl
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2024-03-04 17:09:13,966:INFO - πŸ’Ύ Downloading model [28/44]: Healthy_Adult_Heart.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
2024-03-04 17:09:36,194:INFO - πŸ”¬ Input data has 13999 cells and 9940 genes
2024-03-04 17:09:36,195:INFO - πŸ”— Matching reference genes in the model
2024-03-04 17:09:37,299:INFO - 🧬 3700 features used for prediction
2024-03-04 17:09:37,304:INFO - βš–οΈ Scaling input data
2024-03-04 17:09:37,769:INFO - πŸ–‹οΈ Predicting labels
2024-03-04 17:09:37,963:INFO - βœ… Prediction done!
2024-03-04 17:09:37,967:INFO - πŸ‘€ Detected a neighborhood graph in the input object, will run over-clustering on the basis of it
2024-03-04 17:09:37,968:INFO - ⛓️ Over-clustering input data with resolution set to 10
2024-03-04 17:09:44,406:INFO - πŸ—³οΈ Majority voting the predictions
2024-03-04 17:09:44,458:INFO - βœ… Majority voting done!
bt.CellType.inspect(adata.obs["cell_type_celltypist"]);
❗ received 14 unique terms, 13985 empty/duplicated terms are ignored
❗ 14 terms (100.00%) are not validated for name: Intermediate macrophages, B cells, Tcm/Naive helper T cells, Tem/Effector helper T cells, Tem/Trm cytotoxic T cells, Non-classical monocytes, Regulatory T cells, Tcm/Naive cytotoxic T cells, CD16+ NK cells, pDC, DC2, HSC/MPP, DC, Classical monocytes
   detected 2 CellType terms in Bionty as synonyms: 'pDC', 'DC2'
β†’  add records from Bionty to your CellType registry via .from_values()
   couldn't validate 14 terms: 'CD16+ NK cells', 'Tem/Trm cytotoxic T cells', 'Non-classical monocytes', 'Classical monocytes', 'B cells', 'Tcm/Naive cytotoxic T cells', 'Regulatory T cells', 'pDC', 'HSC/MPP', 'DC', 'DC2', 'Tem/Effector helper T cells', 'Intermediate macrophages', 'Tcm/Naive helper T cells'
β†’  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
_images/3f0eeffee39d7116ce0e00c4e02acfd5e6426bf1fc2b58c310cf4b4d041798ef.png

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 99.454697 7.132596 0.0 0.0
1 ISG20 96.735077 5.074247 0.0 0.0
2 IFI6 94.970886 5.828556 0.0 0.0
3 IFIT3 92.481873 7.432268 0.0 0.0
4 IFIT1 90.698669 8.053500 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), (937, 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);
_images/e0825cef5412ac47a3e8c7f1eb86f587d64db42d9c8a30e20dcb7b45b2d640e4.png
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);
_images/aa6582d4277174ad053eadb63fe4ef572d2216cf0cd22b37fcdafd578bff87f4.png

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"
)

Querying metadata#

artifact.describe()
Artifact(uid='VCpiReSX1w22KW3IRkVF', suffix='.h5ad', accessor='AnnData', description='seurat_ifnb_activated_Bcells', size=215519578, hash='0Dh6_MzW9h9302sNEUGos9', hash_type='sha1-fl', visibility=1, key_is_virtual=True, updated_at=2024-03-04 17:10:10 UTC)

Provenance:
  πŸ—ƒοΈ storage: Storage(uid='aaElNbpY', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/use-cases-registries', type='local', updated_at=2024-03-04 17:06:07 UTC, created_by_id=1)
  πŸ’« transform: Transform(uid='hsPU1OENv0LS6K79', name='Standardize metadata on-the-fly', short_name='analysis-registries', version='0', type=notebook, updated_at=2024-03-04 17:06:58 UTC, created_by_id=1)
  πŸ‘£ run: Run(uid='FcRMckoaPABllIB4Y4wN', run_at=2024-03-04 17:06:58 UTC, transform_id=1, created_by_id=1)
  πŸ‘€ created_by: User(uid='DzTjkKse', handle='testuser1', name='Test User1', updated_at=2024-03-04 17:06:07 UTC)
Features:
  var: FeatureSet(uid='sVSYDGdfynXLME6JzSWS', n=11283, type='number', registry='bionty.Gene', hash='ie3m97I-0Zhg1WIT73pD', updated_at=2024-03-04 17:10:10 UTC, created_by_id=1)
    'CCDC32', 'P2RY10', 'RGS2', 'CSNK2A3', 'GALM', 'MTR', 'CNNM4', 'RABL2B', 'GADD45A', 'RNF139', 'RNF139', 'ANAPC5', 'ST3GAL3', 'ANKRD13C', 'MIGA2', 'ING4', 'XAB2', 'OXA1L', 'GNL3L', 'TCEAL1', ...
  obs: FeatureSet(uid='BTwqcKWhkM2GDZfbyOUD', n=2, registry='core.Feature', hash='WTOaopy-_dt_dwHvMTit', updated_at=2024-03-04 17:10:10 UTC, created_by_id=1)
    πŸ”— stim (2, core.ULabel): 'STIM', 'CTRL'
    πŸ”— cell_type_celltypist (1, bionty.CellType): 'plasmacytoid dendritic cell'
  STIM-up-DEGs: FeatureSet(uid='ypbftJeD6HhETqxNbP86', name='Up-regulated DEGs STIM vs CTRL', n=661, type='category', registry='bionty.Gene', hash='eCmtAvTQ97-SltBxnFI1', updated_at=2024-03-04 17:10:12 UTC, created_by_id=1)
    'B2M', 'HAVCR2', 'B2M', 'HSPA1B', 'HSPA1B', 'GALM', 'TAPBP', 'C9orf72', 'TAPBP', 'TAPBP', 'TAPBP', 'TAPBP', 'HERC5', 'COX17', 'SPART', 'HSPA1B', 'HSPA1B', 'MYDGF', 'CASP1', 'DNAAF1', ...
  STIM-down-DEGs: FeatureSet(uid='V68QvMzrUpczOMmtwndX', name='Down-regulated DEGs STIM vs CTRL', n=1094, type='category', registry='bionty.Gene', hash='N0yMpw8ud_6QGqYk2M16', updated_at=2024-03-04 17:10:12 UTC, created_by_id=1)
    'JUNB', 'RPS5', 'RGS2', 'CORO1A', 'SWAP70', 'TBXAS1', 'P4HB', 'NDUFA13', 'LIMD2', 'TMEM167A', 'BIN1', 'UBE2I', 'HLA-DRA', 'HLA-DRA', 'HLA-DRA', 'HLA-DRA', 'HLA-DRA', 'HLA-DRA', 'HLA-DRA', 'HLA-DRA', ...
Labels:
  🏷️ cell_types (1, bionty.CellType): 'plasmacytoid dendritic cell'
  🏷️ ulabels (2, core.ULabel): 'STIM', 'CTRL'

Querying cell types#

Querying for cell types contains β€œB 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=2024-03-04 17:09:46 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 VCpiReSX1w22KW3IRkVF 1 None .h5ad AnnData seurat_ifnb_activated_Bcells None 215519578 0Dh6_MzW9h9302sNEUGos9 sha1-fl None None 1 1 1 True 2024-03-04 17:10:08.946501+00:00 2024-03-04 17:10:10.385579+00:00 1

Querying pathways#

Querying for pathways contains β€œinterferon-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 2024-03-04 17:06:23.725156+00:00 2024-03-04 17:06:23.725164+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 2024-03-04 17:06:23.867178+00:00 2024-03-04 17:06:23.867190+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 2024-03-04 17:06:23.967986+00:00 2024-03-04 17:06:23.967995+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 2024-03-04 17:06:24.088020+00:00 2024-03-04 17:06:24.088031+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 2024-03-04 17:06:24.151349+00:00 2024-03-04 17:06:24.151358+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 2024-03-04 17:06:23.790053+00:00 2024-03-04 17:06:23.790061+00:00 1

Query artifacts from a pathway:

ln.Artifact.filter(feature_sets__pathways__name__icontains="interferon-beta").first()
Artifact(uid='VCpiReSX1w22KW3IRkVF', suffix='.h5ad', accessor='AnnData', description='seurat_ifnb_activated_Bcells', size=215519578, hash='0Dh6_MzW9h9302sNEUGos9', hash_type='sha1-fl', visibility=1, key_is_virtual=True, updated_at=2024-03-04 17:10:10 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=2024-03-04 17:06:24 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")
['IFI16',
 'PLSCR1',
 'OAS1',
 'AIM2',
 'IRF1',
 'IFITM3',
 'IFITM2',
 'XAF1',
 'SHFL',
 'PNPT1',
 'MNDA',
 'IFITM1',
 'BST2',
 'STAT1',
 'CALM1']
# clean up test instance
!lamin delete --force use-cases-registries
!rm -r ./use-cases-registries
Hide code cell output
πŸ’‘ deleting instance testuser1/use-cases-registries
βœ…     deleted instance settings file: /home/runner/.lamin/instance--testuser1--use-cases-registries.env
βœ…     instance cache deleted
βœ…     deleted '.lndb' sqlite file
❗     consider manually deleting your stored data: /home/runner/work/lamin-usecases/lamin-usecases/docs/use-cases-registries