scvi-tools — Single-Cell Deep Generative Models

Overview

scvi-tools is a probabilistic modeling framework for single-cell genomics built on PyTorch. It implements variational autoencoders (VAEs) that learn low-dimensional latent representations of cells while explicitly modeling batch effects, count noise distributions, and multi-modal data. All models share a unified API: setup_anndata() to register data, instantiate the model, train(), then extract latent representations, normalized expression, or differential expression results. Models operate on raw count data in AnnData format and return statistically grounded outputs with uncertainty estimates.

When to Use

• Integrating multiple scRNA-seq batches or studies with probabilistic batch correction that preserves biological variation
• Performing differential expression with uncertainty quantification and composite hypotheses (not just fold-change thresholding)
• Annotating cell types via semi-supervised transfer learning from a partially-labeled reference (scANVI)
• Jointly modeling CITE-seq protein and RNA data to obtain denoised protein estimates and joint embeddings (totalVI)
• Adapting a pretrained model to a new query dataset without full retraining (scARCHES transfer learning)
• Deconvolving spatial transcriptomics spots into cell type proportions using a matched scRNA-seq reference (DestVI)
• Detecting doublets in scRNA-seq data as a QC preprocessing step (Solo)
• Use harmony-batch-correction instead when you need fast linear batch correction (seconds vs minutes) without deep learning overhead
• For multi-modal MuData workflows (joint RNA+ATAC Multiome, combined modality objects), use muon instead
• For standard clustering and visualization without batch effects or probabilistic DE, use scanpy-scrna-seq

Prerequisites

• Python packages: scvi-tools>=1.1, scanpy, anndata
• Data requirements: AnnData (.h5ad) with raw counts — not log-normalized. Store counts in adata.layers["counts"] if adata.X has been normalized.
• Hardware: CPU works for <50k cells; GPU (8GB+ VRAM) recommended for larger datasets. Training time: 5–30 minutes on GPU for 100k cells.

bash
pip install scvi-tools scanpy
# GPU acceleration (recommended for >50k cells)
pip install "scvi-tools[cuda12]"   # or scvi-tools[cuda11]

Quick Start

Minimal scVI batch integration on a built-in example dataset:

python
import scvi
import scanpy as sc

# Load example data with batch labels
adata = scvi.data.heart_cell_atlas_subsampled()

# Preprocessing: filter genes, select HVGs (subset to save training time)
sc.pp.filter_genes(adata, min_counts=3)
adata.layers["counts"] = adata.X.copy()  # preserve raw counts
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata, n_top_genes=2000, batch_key="cell_source", subset=True)

# Register data, train, extract
scvi.model.SCVI.setup_anndata(adata, layer="counts", batch_key="cell_source")
model = scvi.model.SCVI(adata, n_latent=30)
model.train(max_epochs=200, early_stopping=True)

adata.obsm["X_scVI"] = model.get_latent_representation()
sc.pp.neighbors(adata, use_rep="X_scVI")
sc.tl.umap(adata)
sc.tl.leiden(adata, resolution=0.5)
sc.pl.umap(adata, color=["cell_source", "leiden"])
print(f"Latent shape: {adata.obsm['X_scVI'].shape}")
# Latent shape: (14000, 30)

Core API

1. Data Registration (setup_anndata)

All models share the same registration pattern. Call setup_anndata() on the model class before instantiation to tell scvi-tools where to find counts, batch labels, and covariates.

python
import scvi

# Minimal: counts in a layer, batch column in obs
scvi.model.SCVI.setup_anndata(
    adata,
    layer="counts",        # Key in adata.layers with raw counts; None = adata.X
    batch_key="batch",     # Column in adata.obs for technical batch
)

# Extended: additional categorical and continuous covariates
scvi.model.SCVI.setup_anndata(
    adata,
    layer="counts",
    batch_key="batch",
    categorical_covariate_keys=["donor", "protocol"],   # Discrete biological/technical vars
    continuous_covariate_keys=["percent_mito", "log_n_counts"],  # Continuous covariates
)

# Inspect registered summary
print(adata.uns["_scvi"]["summary_stats"])
# {'n_vars': 2000, 'n_cells': 45000, 'n_batch': 6, 'n_extra_categorical_covs': 2, ...}

2. scVI — Batch Correction and Integration

The core unsupervised model. Learns a batch-corrected latent space and a denoised expression layer. Starting point for any multi-batch scRNA-seq analysis.

python
import scvi

model = scvi.model.SCVI(
    adata,
    n_latent=30,              # Latent space dimensions; 10–50 typical
    n_layers=2,               # Hidden layers in encoder/decoder; 1–3
    n_hidden=128,             # Neurons per hidden layer; 64–256
    gene_likelihood="zinb",   # "zinb" (zero-inflated NB), "nb", or "poisson"
    dispersion="gene",        # "gene" or "gene-batch" for batch-specific dispersion
)
model.train(max_epochs=200, early_stopping=True)

# Extract results
latent = model.get_latent_representation()        # ndarray (n_cells, n_latent)
normalized = model.get_normalized_expression(
    library_size=1e4, n_samples=25, return_mean=True
)
print(f"Latent: {latent.shape}")
print(f"Denoised expression: {normalized.shape}")
# Latent: (45000, 30)
# Denoised expression: (45000, 2000)

adata.obsm["X_scVI"] = latent
adata.layers["scvi_normalized"] = normalized

3. scANVI — Semi-Supervised Cell Annotation

Extends scVI with label supervision for cell type transfer learning. Accepts partially labeled data (unannotated cells labeled "Unknown") and predicts cell types for unlabeled cells.

python
import scvi

# Register with cell type labels; mark unannotated cells as "Unknown"
scvi.model.SCANVI.setup_anndata(
    adata,
    layer="counts",
    batch_key="batch",
    labels_key="cell_type",           # Column in adata.obs with known labels
    unlabeled_category="Unknown",     # Sentinel value for unannotated cells
)

# Recommended: initialize from a pretrained scVI model
scvi_model = scvi.model.SCVI(adata, n_latent=30)
scvi_model.train(max_epochs=200, early_stopping=True)

model = scvi.model.SCANVI.from_scvi_model(scvi_model, unlabeled_category="Unknown")
model.train(max_epochs=20)  # Short fine-tuning on top of scVI

# Predict cell types
labels_pred = model.predict()              # Hard labels (str per cell)
probs = model.predict(soft=True)           # DataFrame: probability per cell type
confidence = probs.max(axis=1)

adata.obs["scANVI_pred"] = labels_pred
adata.obs["scANVI_confidence"] = confidence
print(f"Median confidence: {confidence.median():.3f}")
print(f"High-confidence cells (>0.7): {(confidence > 0.7).sum()}")

4. totalVI — CITE-seq RNA + Protein

Joint probabilistic model for CITE-seq data. Denoises both RNA and protein counts and estimates protein foreground probability (signal vs background).

python
import scvi

# Protein counts must be in adata.obsm (not adata.X or layers)
# adata.obsm["protein_expression"] shape: (n_cells, n_proteins)
scvi.model.TOTALVI.setup_anndata(
    adata,
    layer="counts",
    batch_key="batch",
    protein_expression_obsm_key="protein_expression",
)

model = scvi.model.TOTALVI(adata, latent_distribution="normal")
model.train(max_epochs=200, early_stopping=True)

# Extract joint latent space (RNA + protein)
latent = model.get_latent_representation()

# Denoised RNA and protein separately
rna_norm, protein_norm = model.get_normalized_expression(
    n_samples=25, return_mean=True
)

# Protein foreground probability (probability that signal > background)
foreground_prob = model.get_protein_foreground_probability(n_samples=25, return_mean=True)
print(f"RNA normalized: {rna_norm.shape}")
print(f"Protein normalized: {protein_norm.shape}")
print(f"Foreground prob: {foreground_prob.shape}")  # (n_cells, n_proteins)

adata.obsm["X_totalVI"] = latent

5. Differential Expression

Probabilistic DE using the generative model rather than raw counts. Supports composite hypotheses testing whether effect size exceeds a biologically meaningful threshold (delta).

python
# DE between two cell type groups
de_df = model.differential_expression(
    groupby="cell_type",
    group1="CD4 T",         # Numerator group
    group2="CD8 T",         # Denominator group; None = rest of cells
    mode="change",          # "change": composite |LFC| > delta; "vanilla": any LFC
    delta=0.25,             # Minimum |LFC| to be considered DE
    fdr_target=0.05,        # FDR level for calling significance
    all_stats=True,         # Include mean expression per group
)

# Filter significant DE genes
sig = de_df[de_df["is_de_fdr_0.05"] & (de_df["lfc_mean"].abs() > 0.5)]
sig_sorted = sig.sort_values("lfc_mean", ascending=False)
print(f"Total DE genes (FDR<5%, |LFC|>0.5): {len(sig)}")
print(sig_sorted[["lfc_mean", "bayes_factor", "proba_de"]].head(10))

python
# One-vs-rest DE across all clusters (generates a dict of DataFrames)
de_all = {}
for ct in adata.obs["cell_type"].unique():
    de_all[ct] = model.differential_expression(
        idx1=[adata.obs["cell_type"] == ct],   # Boolean index
        mode="change",
        delta=0.25,
    )
    sig_n = de_all[ct]["is_de_fdr_0.05"].sum()
    print(f"  {ct}: {sig_n} DE genes vs rest")

6. scARCHES — Query-to-Reference Transfer Learning

Adapts a pretrained reference model to a new query dataset without re-training from scratch. Preserves the reference embedding structure and maps query cells into the same latent space.

python
import scvi

# --- Reference training (done once, save the model) ---
scvi.model.SCVI.setup_anndata(ref_adata, layer="counts", batch_key="batch")
ref_model = scvi.model.SCVI(ref_adata, n_latent=30)
ref_model.train(max_epochs=200, early_stopping=True)
ref_model.save("./reference_model/", overwrite=True)

# Reference annotation (use scANVI for label transfer)
scvi.model.SCANVI.setup_anndata(
    ref_adata, layer="counts", batch_key="batch",
    labels_key="cell_type", unlabeled_category="Unknown",
)
ref_scanvi = scvi.model.SCANVI.from_scvi_model(ref_model, unlabeled_category="Unknown")
ref_scanvi.train(max_epochs=20)
ref_scanvi.save("./reference_scanvi/", overwrite=True)

# --- Query mapping (new dataset, minimal training) ---
ref_scanvi = scvi.model.SCANVI.load("./reference_scanvi/", adata=ref_adata)

# Prepare query: must share gene set with reference
query_adata.obs["cell_type"] = "Unknown"   # All query cells are unlabeled
query_model = scvi.model.SCANVI.load_query_data(query_adata, ref_scanvi)
query_model.train(
    max_epochs=100,
    plan_kwargs={"weight_decay": 0.0},  # Critical: prevents catastrophic forgetting
)

# Map query into reference latent space
query_latent = query_model.get_latent_representation(query_adata)
query_labels = query_model.predict(query_adata)
print(f"Query latent: {query_latent.shape}")
print(f"Query label predictions: {query_labels[:5]}")

7. Downstream Analysis on Latent Space

After extracting the latent representation, use scanpy for UMAP, clustering, and marker genes — with the batch-corrected embedding as input.

python
import scanpy as sc

# UMAP and clustering on scVI latent representation
adata.obsm["X_scVI"] = model.get_latent_representation()

sc.pp.neighbors(
    adata,
    use_rep="X_scVI",   # Use scVI latent (not PCA)
    n_neighbors=30,
    n_pcs=None,         # n_pcs ignored when use_rep is set
)
sc.tl.umap(adata)
sc.tl.leiden(adata, resolution=0.5)

# Marker genes using raw counts or normalized expression
# Option A: scanpy Wilcoxon on log-normalized expression
sc.tl.rank_genes_groups(adata, groupby="leiden", method="wilcoxon", n_genes=25)

# Option B: scVI probabilistic DE per cluster (more accurate)
markers = {}
for cluster in adata.obs["leiden"].unique():
    de = model.differential_expression(
        idx1=[adata.obs["leiden"] == cluster],
        mode="change", delta=0.25,
    )
    markers[cluster] = de[de["is_de_fdr_0.05"]].sort_values("lfc_mean", ascending=False)

# Visualization
sc.pl.umap(adata, color=["leiden", "batch", "cell_type"], ncols=3)
sc.pl.rank_genes_groups_dotplot(adata, n_genes=5, groupby="leiden")

Key Concepts

Unified API Pattern

All scvi-tools models follow the same 4-step workflow:

1. ModelClass.setup_anndata(adata, ...)   →  Register layers, batch keys, covariates
2. model = ModelClass(adata, ...)         →  Set architecture hyperparameters
3. model.train(max_epochs=..., ...)       →  Fit model (GPU auto-detected)
4. model.get_*()                          →  Extract results: latent, normalized, DE

Model Selection Guide

| Data | Model | Core Feature | When to Use | |------|-------|--------------|-------------| | scRNA-seq | scVI | Batch correction, denoising | Default for any multi-batch scRNA-seq | | scRNA-seq (partial labels) | scANVI | Cell type transfer | Have reference labels; want to annotate query | | CITE-seq (RNA+protein) | totalVI | Joint RNA+protein | 10x CITE-seq, REAP-seq | | Reference → query | scARCHES | Transfer learning | Map new data to existing atlas | | Spatial | DestVI | Spot deconvolution | 10x Visium, Slide-seq | | scRNA-seq QC | Solo | Doublet detection | Pre-analysis QC step |

Raw Counts Requirement

scvi-tools models learn count distributions directly from raw data. Log-normalized input produces incorrect results. Always preserve raw counts before normalization:

python
# Correct workflow: save counts, then normalize for scanpy steps
adata.layers["counts"] = adata.X.copy()   # Save raw before any transformation
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
# Then: scvi.model.SCVI.setup_anndata(adata, layer="counts", ...)

Common Workflows

Workflow 1: Multi-Batch scRNA-seq Integration

Goal: Integrate multiple scRNA-seq datasets, remove batch effects, identify cell clusters, and find marker genes.

python
import scvi
import scanpy as sc

# Load and concatenate datasets
adata1 = sc.read_h5ad("dataset1.h5ad")
adata2 = sc.read_h5ad("dataset2.h5ad")
adata3 = sc.read_h5ad("dataset3.h5ad")
adata = sc.concat(
    [adata1, adata2, adata3],
    label="batch",
    keys=["dataset1", "dataset2", "dataset3"],
)

# Preprocessing: preserve raw counts, select HVGs per batch
adata.layers["counts"] = adata.X.copy()
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(
    adata,
    n_top_genes=4000,
    batch_key="batch",       # Select HVGs consistently across batches
    subset=True,
)
print(f"HVGs selected: {adata.n_vars}")

# scVI integration
scvi.model.SCVI.setup_anndata(adata, layer="counts", batch_key="batch")
model = scvi.model.SCVI(adata, n_latent=30, n_layers=2)
model.train(max_epochs=300, early_stopping=True, early_stopping_patience=15)
print(f"Training history: {model.history['elbo_train'].tail()}")

# Batch-corrected analysis
adata.obsm["X_scVI"] = model.get_latent_representation()
adata.layers["scvi_norm"] = model.get_normalized_expression(n_samples=25, return_mean=True)

sc.pp.neighbors(adata, use_rep="X_scVI", n_neighbors=30)
sc.tl.umap(adata)
sc.tl.leiden(adata, resolution=0.5)

# Verify batch mixing (batches should overlap on UMAP)
sc.pl.umap(adata, color=["batch", "leiden"], save="_batch_integration.png")
model.save("./scvi_model/", overwrite=True)
print(f"Clusters: {adata.obs['leiden'].nunique()}")

Workflow 2: CITE-seq totalVI Pipeline

Goal: Model CITE-seq RNA + protein data jointly, obtain denoised protein estimates, and generate a joint embedding for annotation.

python
import scvi
import scanpy as sc
import pandas as pd

# Load CITE-seq AnnData (RNA in X, protein counts in obsm)
adata = sc.read_h5ad("citeseq_data.h5ad")
# Expected structure:
#   adata.X or adata.layers["counts"] : RNA raw counts (n_cells, n_genes)
#   adata.obsm["protein_expression"]   : Protein raw counts (n_cells, n_proteins)

# Preprocessing
adata.layers["counts"] = adata.X.copy()
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata, n_top_genes=4000, batch_key="batch", subset=True)

# totalVI setup and training
scvi.model.TOTALVI.setup_anndata(
    adata,
    layer="counts",
    batch_key="batch",
    protein_expression_obsm_key="protein_expression",
)

model = scvi.model.TOTALVI(adata, latent_distribution="normal")
model.train(max_epochs=200, early_stopping=True)

# Extract joint embedding and denoised values
adata.obsm["X_totalVI"] = model.get_latent_representation()
rna_norm, protein_norm = model.get_normalized_expression(n_samples=25, return_mean=True)
foreground = model.get_protein_foreground_probability(n_samples=25, return_mean=True)

adata.layers["rna_denoised"] = rna_norm
protein_df = pd.DataFrame(
    protein_norm,
    index=adata.obs_names,
    columns=adata.uns["protein_names"],
)
protein_df.to_csv("denoised_protein_expression.csv")
print(f"Protein foreground: min={foreground.min():.3f}, max={foreground.max():.3f}")

# Joint clustering on RNA + protein latent
sc.pp.neighbors(adata, use_rep="X_totalVI", n_neighbors=30)
sc.tl.umap(adata)
sc.tl.leiden(adata, resolution=0.8)

# Protein-guided annotation (use denoised protein for clean signal)
protein_markers = {"B cell": "CD19", "T cell": "CD3E", "Monocyte": "CD14"}
for cell_type, marker in protein_markers.items():
    if marker in protein_df.columns:
        adata.obs[f"denoised_{marker}"] = protein_df[marker].values
sc.pl.umap(adata, color=["leiden"] + [f"denoised_{m}" for m in protein_markers.values()])

Key Parameters

| Parameter | Model / Function | Default | Range / Options | Effect | |-----------|-----------------|---------|-----------------|--------| | n_latent | All models | 10 | 10–50 | Latent space dimensionality; 20–30 typical for most datasets | | n_layers | All models | 1 | 1–3 | Depth of encoder/decoder networks | | n_hidden | All models | 128 | 64–256 | Width of each hidden layer | | gene_likelihood | scVI, scANVI | "zinb" | "zinb", "nb", "poisson" | Count noise distribution; "nb" faster if sparsity is low | | max_epochs | train() | 400 | 50–1000 | Training iterations (ELBO steps) | | early_stopping | train() | False | True/False | Halt when validation ELBO plateaus | | early_stopping_patience | train() | 45 | 5–100 | Epochs to wait before stopping | | batch_size | train() | 128 | 64–512 | Mini-batch size; reduce if OOM | | lr | train() | 1e-3 | 1e-4–1e-2 | Initial learning rate | | delta | differential_expression() | 0.25 | 0.1–1.0 | Min |LFC| threshold for composite DE | | n_samples | get_normalized_expression() | 1 | 1–100 | Posterior samples; ≥25 for stable estimates |

Best Practices

1. Always store raw counts before normalization: Run adata.layers["counts"] = adata.X.copy() immediately after loading data, before any normalize_total or log1p call. Passing log-normalized data silently produces incorrect latent spaces.

1. Select highly variable genes before training: Use sc.pp.highly_variable_genes(n_top_genes=2000–4000, batch_key="batch") to reduce training time and model noise. Including all genes rarely improves results and significantly slows training.

1. Register all known batch variables: If data has multiple technical sources (sequencing plate, donor, protocol), include them as batch_key or categorical_covariate_keys. Unregistered batch effects contaminate the latent space and downstream DE.

1. Use the scVI → scANVI two-step pipeline: Initializing scANVI with from_scvi_model() after training scVI is faster, more stable, and produces better embeddings than training scANVI from scratch. Always train scVI first when using scANVI.

1. Save trained models immediately: model.save("./model_dir/") persists the full model. Reloading with SCVI.load() is seconds vs. minutes of retraining. Models are typically 10–50 MB.

1. Use n_latent=20–30 as starting point: Values below 10 lose resolution; above 50 tend to overfit without added biological insight. Increase to 50 only for very heterogeneous atlases (>500k cells, many cell types).

1. Filter low-confidence scANVI predictions: Cells where predict(soft=True).max(axis=1) < 0.7 are ambiguous; treat them as "Unknown" rather than accepting the argmax label.

Common Recipes

Recipe: Doublet Detection with Solo

When to use: Remove doublets before integration or DE to avoid spurious clusters.

python
import scvi

scvi.model.SCVI.setup_anndata(adata, layer="counts")
vae = scvi.model.SCVI(adata, n_latent=20)
vae.train(max_epochs=100)

solo = scvi.external.SOLO.from_scvi_model(vae)
solo.train(max_epochs=200)
doublet_preds = solo.predict()   # DataFrame: {"singlet": prob, "doublet": prob}
adata.obs["doublet_score"] = doublet_preds["doublet"].values
adata.obs["is_doublet"] = doublet_preds["prediction"].values

n_doublets = (adata.obs["is_doublet"] == "doublet").sum()
print(f"Detected {n_doublets} doublets ({n_doublets / adata.n_obs:.1%})")
adata = adata[adata.obs["is_doublet"] == "singlet"].copy()
print(f"Cells after doublet removal: {adata.n_obs}")

Recipe: Spatial Deconvolution with DestVI

When to use: Estimate cell type proportions in spatial transcriptomics spots using a matched scRNA-seq reference.

python
import scvi

# Step 1: Train CondSCVI on reference single-cell data
scvi.model.CondSCVI.setup_anndata(sc_adata, layer="counts", labels_key="cell_type")
sc_model = scvi.model.CondSCVI(sc_adata, weight_obs=False)
sc_model.train(max_epochs=200)

# Step 2: Deconvolve spatial spots
scvi.model.DestVI.setup_anndata(st_adata, layer="counts")
st_model = scvi.model.DestVI.from_rna_model(st_adata, sc_model)
st_model.train(max_epochs=2500)

proportions = st_model.get_proportions()  # DataFrame (n_spots, n_cell_types)
st_adata.obsm["cell_type_proportions"] = proportions.values
print(f"Proportions per spot: {proportions.shape}")
print(proportions.head())

Recipe: Extract Denoised Expression for Imputation

When to use: Obtain smooth, denoised expression values for visualization or downstream machine learning when raw sparse counts are too noisy.

python
import scvi

scvi.model.SCVI.setup_anndata(adata, layer="counts", batch_key="batch")
model = scvi.model.SCVI(adata, n_latent=30)
model.train(max_epochs=200, early_stopping=True)

# n_samples >= 25 gives stable posterior mean; increase to 100 for publication
denoised = model.get_normalized_expression(
    n_samples=50,
    library_size="latent",  # "latent" uses learned library size; int for fixed normalization
    return_mean=True,
)
adata.layers["denoised"] = denoised
print(f"Denoised layer added: {denoised.shape}")
# Use adata.layers["denoised"] for heatmaps, pseudotime, or ML features

Troubleshooting

| Problem | Cause | Solution | |---------|-------|----------| | ValueError: adata must contain raw counts | Log-normalized data passed instead of raw counts | Save raw before normalizing: adata.layers["counts"] = adata.X.copy() then setup_anndata(layer="counts") | | Training loss oscillates without decreasing | Learning rate too high or very heterogeneous data | Try lr=1e-4; check that adata.X and the registered layer are not sparse with negatives | | CUDA out of memory | Dataset too large for GPU VRAM | Reduce batch_size=64, n_hidden=64; subset to fewer HVGs; use CPU for small datasets | | Poor batch integration (batches separate on UMAP) | Batch key not registered, or too few epochs | Verify batch_key column exists in adata.obs; increase max_epochs; add early_stopping=True | | scANVI predicts same label for all cells | Too few labeled cells per type or learning rate issue | Need ≥50 labeled cells per type; use from_scvi_model() workflow; check unlabeled_category matches exactly | | load() fails with KeyError or shape mismatch | AnnData var_names differ from training data | Ensure query adata.var_names exactly matches training data; do not subset genes after training | | Slow training on CPU for large datasets | Large dataset without GPU acceleration | Install scvi-tools[cuda12]; pass accelerator="gpu" to train(); or subsample to 50k cells for prototyping | | scvi.model.SCANVI.load_query_data shape error | Query and reference have different gene sets | Align genes: query_adata = query_adata[:, ref_adata.var_names] before calling load_query_data |

Related Skills

• scanpy-scrna-seq — standard scRNA-seq QC, clustering, marker genes; use scVI latent as input to sc.pp.neighbors(use_rep="X_scVI")
• anndata-data-structure — create, subset, concatenate, and persist AnnData objects required by scvi-tools
• harmony-batch-correction — fast linear batch correction alternative; use when deep learning overhead is not justified
• cellxgene-census — download reference atlases for scANVI transfer learning and scARCHES query-to-reference mapping
• muon-multiomics-singlecell — multi-modal single-cell analysis with MuData; complement to totalVI for Multiome workflows

References

• scvi-tools documentation — official API reference, tutorials, and model guides
• scvi-tools GitHub — source code, issue tracker, and changelog
• Lopez et al. (2018) "Deep generative modeling for single-cell transcriptomics" — Nature Methods 15:1053–1058 — original scVI paper
• Xu et al. (2021) "Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models" — Molecular Systems Biology 17:e9620 — scANVI paper
• Gayoso et al. (2022) "A Python library for probabilistic analysis of single-cell omics data" — Nature Biotechnology 40:163–166 — scvi-tools library paper