gh-k-dense-ai-claude-scientific-skills-scientific-skills/skills/scvi-tools/references/models-scrna-seq.md

Single-Cell RNA-seq Models

This document covers core models for analyzing single-cell RNA sequencing data in scvi-tools.

scVI (Single-Cell Variational Inference)

Purpose: Unsupervised analysis, dimensionality reduction, and batch correction for scRNA-seq data.

Key Features:

• Deep generative model based on variational autoencoders (VAE)
• Learns low-dimensional latent representations that capture biological variation
• Automatically corrects for batch effects and technical covariates
• Enables normalized gene expression estimation
• Supports differential expression analysis

When to Use:

• Initial exploration and dimensionality reduction of scRNA-seq datasets
• Integrating multiple batches or studies
• Generating batch-corrected expression matrices
• Performing probabilistic differential expression analysis

Basic Usage:

import scvi

# Setup data
scvi.model.SCVI.setup_anndata(
    adata,
    layer="counts",
    batch_key="batch"
)

# Train model
model = scvi.model.SCVI(adata, n_latent=30)
model.train()

# Extract results
latent = model.get_latent_representation()
normalized = model.get_normalized_expression()

Key Parameters:

• n_latent: Dimensionality of latent space (default: 10)
• n_layers: Number of hidden layers (default: 1)
• n_hidden: Number of nodes per hidden layer (default: 128)
• dropout_rate: Dropout rate for neural networks (default: 0.1)
• dispersion: Gene-specific or cell-specific dispersion ("gene" or "gene-batch")
• gene_likelihood: Distribution for data ("zinb", "nb", "poisson")

Outputs:

• get_latent_representation(): Batch-corrected low-dimensional embeddings
• get_normalized_expression(): Denoised, normalized expression values
• differential_expression(): Probabilistic DE testing between groups
• get_feature_correlation_matrix(): Gene-gene correlation estimates

scANVI (Single-Cell ANnotation using Variational Inference)

Purpose: Semi-supervised cell type annotation and integration using labeled and unlabeled cells.

Key Features:

• Extends scVI with cell type labels
• Leverages partially labeled datasets for annotation transfer
• Performs simultaneous batch correction and cell type prediction
• Enables query-to-reference mapping

When to Use:

• Annotating new datasets using reference labels
• Transfer learning from well-annotated to unlabeled datasets
• Joint analysis of labeled and unlabeled cells
• Building cell type classifiers with uncertainty quantification

Basic Usage:

# Option 1: Train from scratch
scvi.model.SCANVI.setup_anndata(
    adata,
    layer="counts",
    batch_key="batch",
    labels_key="cell_type",
    unlabeled_category="Unknown"
)
model = scvi.model.SCANVI(adata)
model.train()

# Option 2: Initialize from pretrained scVI
scvi_model = scvi.model.SCVI(adata)
scvi_model.train()
scanvi_model = scvi.model.SCANVI.from_scvi_model(
    scvi_model,
    unlabeled_category="Unknown"
)
scanvi_model.train()

# Predict cell types
predictions = scanvi_model.predict()

Key Parameters:

• labels_key: Column in adata.obs containing cell type labels
• unlabeled_category: Label for cells without annotations
• All scVI parameters are also available

Outputs:

• predict(): Cell type predictions for all cells
• predict_proba(): Prediction probabilities
• get_latent_representation(): Cell type-aware latent space

AUTOZI

Purpose: Automatic identification and modeling of zero-inflated genes in scRNA-seq data.

Key Features:

• Distinguishes biological zeros from technical dropout
• Learns which genes exhibit zero-inflation
• Provides gene-specific zero-inflation probabilities
• Improves downstream analysis by accounting for dropout

When to Use:

• Detecting which genes are affected by technical dropout
• Improving imputation and normalization for sparse datasets
• Understanding the extent of zero-inflation in your data

Basic Usage:

scvi.model.AUTOZI.setup_anndata(adata, layer="counts")
model = scvi.model.AUTOZI(adata)
model.train()

# Get zero-inflation probabilities per gene
zi_probs = model.get_alphas_betas()

VeloVI

Purpose: RNA velocity analysis using variational inference.

Key Features:

• Joint modeling of spliced and unspliced RNA counts
• Probabilistic estimation of RNA velocity
• Accounts for technical noise and batch effects
• Provides uncertainty quantification for velocity estimates

When to Use:

• Inferring cellular dynamics and differentiation trajectories
• Analyzing spliced/unspliced count data
• RNA velocity analysis with batch correction

Basic Usage:

import scvelo as scv

# Prepare velocity data
scv.pp.filter_and_normalize(adata)
scv.pp.moments(adata)

# Train VeloVI
scvi.model.VELOVI.setup_anndata(adata, spliced_layer="Ms", unspliced_layer="Mu")
model = scvi.model.VELOVI(adata)
model.train()

# Get velocity estimates
latent_time = model.get_latent_time()
velocities = model.get_velocity()

contrastiveVI

Purpose: Isolating perturbation-specific variations from background biological variation.

Key Features:

• Separates shared variation (common across conditions) from target-specific variation
• Useful for perturbation studies (drug treatments, genetic perturbations)
• Identifies condition-specific gene programs
• Enables discovery of treatment-specific effects

When to Use:

• Analyzing perturbation experiments (drug screens, CRISPR, etc.)
• Identifying genes responding specifically to treatments
• Separating treatment effects from background variation
• Comparing control vs. perturbed conditions

Basic Usage:

scvi.model.CONTRASTIVEVI.setup_anndata(
    adata,
    layer="counts",
    batch_key="batch",
    categorical_covariate_keys=["condition"]  # control vs treated
)

model = scvi.model.CONTRASTIVEVI(
    adata,
    n_latent=10,        # Shared variation
    n_latent_target=5   # Target-specific variation
)
model.train()

# Extract representations
shared = model.get_latent_representation(representation="shared")
target_specific = model.get_latent_representation(representation="target")

CellAssign

Purpose: Marker-based cell type annotation using known marker genes.

Key Features:

• Uses prior knowledge of marker genes for cell types
• Probabilistic assignment of cells to types
• Handles marker gene overlap and ambiguity
• Provides soft assignments with uncertainty

When to Use:

• Annotating cells using known marker genes
• Leveraging existing biological knowledge for classification
• Cases where marker gene lists are available but reference datasets are not

Basic Usage:

# Create marker gene matrix (cell types x genes)
marker_gene_mat = pd.DataFrame({
    "CD4 T cells": [1, 1, 0, 0],  # CD3D, CD4, CD8A, CD19
    "CD8 T cells": [1, 0, 1, 0],
    "B cells": [0, 0, 0, 1]
}, index=["CD3D", "CD4", "CD8A", "CD19"])

scvi.model.CELLASSIGN.setup_anndata(adata, layer="counts")
model = scvi.model.CELLASSIGN(adata, marker_gene_mat)
model.train()

predictions = model.predict()

Solo (Doublet Detection)

Purpose: Identifying doublets (cells containing two or more cells) in scRNA-seq data.

Key Features:

• Semi-supervised doublet detection using scVI embeddings
• Simulates artificial doublets for training
• Provides doublet probability scores
• Can be applied to any scVI model

When to Use:

• Quality control of scRNA-seq datasets
• Removing doublets before downstream analysis
• Assessing doublet rates in your data

Basic Usage:

# Train scVI model first
scvi.model.SCVI.setup_anndata(adata, layer="counts")
scvi_model = scvi.model.SCVI(adata)
scvi_model.train()

# Train Solo for doublet detection
solo_model = scvi.external.SOLO.from_scvi_model(scvi_model)
solo_model.train()

# Predict doublets
predictions = solo_model.predict()
doublet_scores = predictions["doublet"]
adata.obs["doublet_score"] = doublet_scores

Amortized LDA (Topic Modeling)

Purpose: Topic modeling for gene expression using Latent Dirichlet Allocation.

Key Features:

• Discovers gene expression programs (topics)
• Amortized variational inference for scalability
• Each cell is a mixture of topics
• Each topic is a distribution over genes

When to Use:

• Discovering gene programs or expression modules
• Understanding compositional structure of expression
• Alternative dimensionality reduction approach
• Interpretable decomposition of expression patterns

Basic Usage:

scvi.model.AMORTIZEDLDA.setup_anndata(adata, layer="counts")
model = scvi.model.AMORTIZEDLDA(adata, n_topics=10)
model.train()

# Get topic compositions per cell
topic_proportions = model.get_latent_representation()

# Get gene loadings per topic
topic_gene_loadings = model.get_topic_distribution()

Model Selection Guidelines

Choose scVI when:

• Starting with unsupervised analysis
• Need batch correction and integration
• Want normalized expression and DE analysis

Choose scANVI when:

• Have some labeled cells for training
• Need cell type annotation
• Want to transfer labels from reference to query

Choose AUTOZI when:

• Concerned about technical dropout
• Need to identify zero-inflated genes
• Working with very sparse datasets

Choose VeloVI when:

• Have spliced/unspliced count data
• Interested in cellular dynamics
• Need RNA velocity with batch correction

Choose contrastiveVI when:

• Analyzing perturbation experiments
• Need to separate treatment effects
• Want to identify condition-specific programs

Choose CellAssign when:

• Have marker gene lists available
• Want probabilistic marker-based annotation
• No reference dataset available

Choose Solo when:

• Need doublet detection
• Already using scVI for analysis
• Want probabilistic doublet scores