bio-machine-learning-atlas-mapping

name	bio-machine-learning-atlas-mapping
description	Maps query single-cell data to reference atlases using scArches transfer learning with scVI and scANVI models. Transfers cell type labels without retraining on combined data. Use when annotating new single-cell datasets using pre-trained reference models.
tool_type	python
primary_tool	scvi-tools

Version Compatibility

Reference examples tested with: anndata 0.10+, scanpy 1.10+, scvi-tools 1.1+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Transfer Learning for Single-Cell Data

"Map my scRNA-seq data onto a reference atlas" → Transfer cell type labels from a pre-trained reference model to query cells using architectural surgery (scArches) without retraining.

• Python: scvi.model.SCVI.load_query_data() → get_latent_representation() → scanpy.tl.ingest()

scVI Reference Mapping (scArches)

Goal: Map query single-cell data onto a pre-trained reference model to obtain a shared latent embedding.

Approach: Load a pre-trained scVI model, prepare query data with matching gene sets, then perform surgical fine-tuning that updates only query-specific parameters.

import scvi
import scanpy as sc

# Load pre-trained reference model
adata_ref = sc.read_h5ad('reference.h5ad')
# Model must have been saved with save_anndata=True
scvi.model.SCVI.setup_anndata(adata_ref, layer='counts', batch_key='batch')
ref_model = scvi.model.SCVI.load('reference_model/', adata=adata_ref)

# Prepare query data
adata_query = sc.read_h5ad('query.h5ad')
# Subset to reference genes
adata_query = adata_query[:, adata_ref.var_names].copy()

# Set up query AnnData using reference setup
scvi.model.SCVI.prepare_query_anndata(adata_query, ref_model)

# Load query into model (creates "surgical" fine-tuned model)
query_model = scvi.model.SCVI.load_query_data(adata_query, ref_model)

# Surgical training: update only query-specific parameters
# weight_decay=0.0: Standard for surgery; prevents reference drift
query_model.train(max_epochs=200, plan_kwargs={'weight_decay': 0.0})

# Get latent representation
adata_query.obsm['X_scVI'] = query_model.get_latent_representation()

scANVI for Label Transfer

Goal: Transfer cell type labels from a labeled reference atlas to unlabeled query data.

Approach: Train a semi-supervised scANVI model on the reference, then map query cells via surgical fine-tuning and predict labels with confidence scores.

import scvi
import scanpy as sc

# Reference with cell type labels
adata_ref = sc.read_h5ad('reference_labeled.h5ad')
scvi.model.SCVI.setup_anndata(adata_ref, layer='counts', batch_key='batch')
ref_vae = scvi.model.SCVI(adata_ref, n_latent=30)
ref_vae.train(max_epochs=100)

# Convert to scANVI (semi-supervised)
scvi.model.SCANVI.setup_anndata(adata_ref, layer='counts', batch_key='batch', labels_key='cell_type', unlabeled_category='Unknown')
ref_scanvi = scvi.model.SCANVI.from_scvi_model(ref_vae, labels_key='cell_type', unlabeled_category='Unknown')
ref_scanvi.train(max_epochs=50)
ref_scanvi.save('reference_scanvi/')

# Map query data
adata_query = sc.read_h5ad('query.h5ad')
adata_query = adata_query[:, adata_ref.var_names].copy()

scvi.model.SCANVI.prepare_query_anndata(adata_query, ref_scanvi)
query_scanvi = scvi.model.SCANVI.load_query_data(adata_query, ref_scanvi)
query_scanvi.train(max_epochs=100, plan_kwargs={'weight_decay': 0.0})

# Transfer labels
adata_query.obs['predicted_cell_type'] = query_scanvi.predict()
adata_query.obsm['X_scANVI'] = query_scanvi.get_latent_representation()

Prediction Confidence

Goal: Assess reliability of transferred labels and flag cells that may represent novel types.

Approach: Extract soft prediction probabilities from the scANVI model and identify low-confidence assignments below a threshold.

# Get prediction probabilities
soft_predictions = query_scanvi.predict(soft=True)
adata_query.obs['prediction_confidence'] = soft_predictions.max(axis=1)

# Flag low-confidence predictions
# confidence < 0.5: May be novel cell type or poor mapping
low_conf = adata_query.obs['prediction_confidence'] < 0.5
print(f'Low confidence predictions: {low_conf.sum()} ({low_conf.mean():.1%})')

Joint Embedding Visualization

Goal: Visualize reference and query cells together to assess integration quality.

Approach: Concatenate reference and query datasets, compute UMAP from the shared latent representation, and color by dataset and cell type.

import scanpy as sc

# Combine reference and query for visualization
adata_combined = adata_ref.concatenate(adata_query, batch_key='dataset', batch_categories=['reference', 'query'])

# Use latent space for neighbors/UMAP
sc.pp.neighbors(adata_combined, use_rep='X_scVI')
sc.tl.umap(adata_combined)
sc.pl.umap(adata_combined, color=['dataset', 'cell_type'], save='_transfer.png')

Pre-trained Reference Atlases

Atlas	Model	URL
Human Lung Cell Atlas	scANVI	cellxgene.cziscience.com
Tabula Sapiens	scVI	tabula-sapiens-portal.ds.czbiohub.org
Mouse Cell Atlas	scVI	bis.zju.edu.cn/MCA

Training Parameters

Parameter	Surgical	Full Retrain	Notes
weight_decay	0.0	0.001	0.0 preserves reference
max_epochs	100-200	200-400	Less for surgery
early_stopping	True	True	Prevents overfitting

Related Skills

• single-cell/cell-annotation - Manual annotation methods
• single-cell/batch-integration - Batch effect correction
• single-cell/preprocessing - Data preparation before transfer