Single Cell Multi Omics Analysis Scvi

Models for analyzing single-cell chromatin data. See references/models-atac-seq.md for:

• PeakVI: Peak-based ATAC-seq analysis and integration
• PoissonVI: Quantitative fragment count modeling
• scBasset: Deep learning approach with motif analysis

3. Multimodal & Multi-omics Integration

Joint analysis of multiple data types. See references/models-multimodal.md for:

• totalVI: CITE-seq protein and RNA joint modeling
• MultiVI: Paired and unpaired multi-omic integration
• MrVI: Multi-resolution cross-sample analysis

4. Spatial Transcriptomics

Spatially-resolved transcriptomics analysis. See references/models-spatial.md for:

• DestVI: Multi-resolution spatial deconvolution
• Stereoscope: Cell type deconvolution
• Tangram: Spatial mapping and integration
• scVIVA: Cell-environment relationship analysis

5. Specialized Modalities

Additional specialized analysis tools. See references/models-specialized.md for:

• MethylVI/MethylANVI: Single-cell methylation analysis
• CytoVI: Flow/mass cytometry batch correction
• Solo: Doublet detection
• CellAssign: Marker-based cell type annotation

Why this exists

This skill provides a unified, statistically rigorous foundation:

• Raw counts directly: No need for arbitrary pseudo-counts or log-normalization steps prior to modeling.
• Unified API: A consistent interface (setup → train → extract) across all models and multi-omic data types.
• Principled Batch Correction: Handles technical variation through explicit covariate registration in the generative model.
• GPU Acceleration: Automatically utilizes available GPUs via PyTorch Lightning to scale to millions of cells.

Usage

All scvi-tools models follow a consistent API pattern:

# 1. Load and preprocess data (AnnData format)
import scvi
import scanpy as sc

adata = scvi.data.heart_cell_atlas_subsampled()
sc.pp.filter_genes(adata, min_counts=3)
sc.pp.highly_variable_genes(adata, n_top_genes=1200)

# 2. Register data with model (specify layers, covariates)
scvi.model.SCVI.setup_anndata(
    adata,
    layer="counts",  # Use raw counts, not log-normalized
    batch_key="batch",
    categorical_covariate_keys=["donor"],
    continuous_covariate_keys=["percent_mito"]
)

# 3. Create and train model
model = scvi.model.SCVI(adata)
model.train()

# 4. Extract latent representations and normalized values
latent = model.get_latent_representation()
normalized = model.get_normalized_expression(library_size=1e4)

# 5. Store in AnnData for downstream analysis
adata.obsm["X_scVI"] = latent
adata.layers["scvi_normalized"] = normalized

# 6. Downstream analysis with scanpy
sc.pp.neighbors(adata, use_rep="X_scVI")
sc.tl.umap(adata)
sc.tl.leiden(adata)

Key Design Principles:

• Raw counts required: Models expect unnormalized count data for optimal performance
• Unified API: Consistent interface across all models (setup → train → extract)
• AnnData-centric: Seamless integration with the scanpy ecosystem
• GPU acceleration: Automatic utilization of available GPUs
• Batch correction: Handle technical variation through covariate registration

Common Analysis Tasks

Differential Expression

Probabilistic DE analysis using the learned generative models:

de_results = model.differential_expression(
    groupby="cell_type",
    group1="TypeA",
    group2="TypeB",
    mode="change",  # Use composite hypothesis testing
    delta=0.25      # Minimum effect size threshold
)

See references/differential-expression.md for detailed methodology and interpretation.

Model Persistence

Save and load trained models:

# Save model
model.save("./model_directory", overwrite=True)

# Load model
model = scvi.model.SCVI.load("./model_directory", adata=adata)

Batch Correction and Integration

Integrate datasets across batches or studies:

# Register batch information
scvi.model.SCVI.setup_anndata(adata, batch_key="study")

# Model automatically learns batch-corrected representations
model = scvi.model.SCVI(adata)
model.train()
latent = model.get_latent_representation()  # Batch-corrected

Example Output

Training Status:
Epoch 1/400:   0%|          | 0/400 [00:00<?, ?it/s]
Epoch 400/400: 100%|██████████| 400/400 [02:15<00:00,  2.95it/s, v_num=1]
Training finished.
Final ELBO loss: 1245.67

Outputs generated:
  1. Model Directory: ./model_directory/
     - model.pt (Trained neural network weights)
     - attr.pkl (Model hyperparameters and architecture)
     - var_names.csv (Features used for training)

  2. Updated AnnData object (latent_anndata.h5ad):
     AnnData object with n_obs × n_vars = 15000 × 1200
       obs: 'batch', 'donor', 'cell_type', '_scvi_batch', '_scvi_labels'
       var: 'highly_variable', 'means', 'variances'
       uns: '_scvi_uuid', '_scvi_manager_uuid'
       obsm: 'X_scVI' (10-dimensional batch-corrected latent space)
       layers: 'counts', 'scvi_normalized' (Denoised expected expression)

Best practice

1. Use raw counts: Always provide unnormalized count data to models
2. Filter genes: Remove low-count genes before analysis (e.g., min_counts=3)
3. Register covariates: Include known technical factors (batch, donor, etc.) in setup_anndata
4. Feature selection: Use highly variable genes for improved performance
5. Model saving: Always save trained models to avoid retraining
6. GPU usage: Enable GPU acceleration for large datasets (accelerator="gpu")
7. Scanpy integration: Store outputs in AnnData objects for downstream analysis

Requirements

Inputs

Outputs

Citations