User

Import scvi-tools as:

import scvi

Model

model.AUTOZI	Automatic identification of zero-inflated genes [Clivio et al., 2019].
model.CondSCVI	Conditional version of single-cell Variational Inference.
model.DestVI	Multi-resolution deconvolution of Spatial Transcriptomics data (DestVI) [Lopez et al., 2022].
model.LinearSCVI	Linearly-decoded VAE [Svensson et al., 2020].
model.PEAKVI	Peak Variational Inference for chromatin accessilibity analysis [Ashuach et al., 2022].
model.SCANVI	Single-cell annotation using variational inference [Xu et al., 2021].
model.SCVI	single-cell Variational Inference [Lopez et al., 2018].
model.TOTALVI	total Variational Inference [Gayoso et al., 2021].
model.MULTIVI	Integration of multi-modal and single-modality data [Ashuach et al., 2023].
model.AmortizedLDA	Amortized Latent Dirichlet Allocation [Blei et al., 2003].
model.JaxSCVI	single-cell Variational Inference [Lopez et al., 2018], but with JAX.
model.mlxSCVI	Single-cell variational inference model using the MLX framework.

External models

external.CellAssign	Reimplementation of CellAssign for reference-based annotation [Zhang et al., 2019].
external.CYTOVI	Variational inference for cytometry (CytoVI).
external.GIMVI	Joint VAE for imputing missing genes in spatial data [Lopez et al., 2019].
external.RNAStereoscope	Reimplementation of Stereoscope [Andersson et al., 2020].
external.SpatialStereoscope	Reimplementation of Stereoscope [Andersson et al., 2020].
external.SOLO	Doublet detection in scRNA-seq [Bernstein et al., 2020].
external.SCAR	Ambient RNA removal in scRNA-seq data [Sheng et al., 2022].
external.Tangram	Reimplementation of Tangram [Biancalani et al., 2021].
external.SCBASSET	Reimplementation of scBasset [Yuan and Kelley, 2022].
external.ContrastiveVI	contrastive variational inference [Weinberger et al., 2023].
external.POISSONVI	Peak Variational Inference using a Poisson distribution [Martens et al., 2023].
external.VELOVI	Velocity Variational Inference [Gayoso et al., 2023].
external.MRVI	Multi-resolution Variational Inference (MrVI).
external.TorchMRVI	Multi-resolution Variational Inference (MrVI) [Boyeau et al., 2025].
external.JaxMRVI	Multi-resolution Variational Inference (MrVI) [Boyeau et al., 2025].
external.METHYLVI	Model class for methylVI [Weinberger and Lee, 2023]
external.METHYLANVI	Methylation annotation using variational inference [Weinberger et al., 2023].
external.Decipher	Decipher model for single-cell data analysis [Nazaret et al., 2024].
external.TOTALANVI	total Variational Inference [Gayoso et al., 2021].
external.RESOLVI	ResolVI addresses noise and bias in single-cell resolved spatial transcriptomics data.
external.SysVI	Integration with cVAE & optional VampPrior and latent cycle-consistency.
external.SCVIVA	scVIVA: variational auto-encoder with niche decoders for ST:cite:p:Levy25.

Data loading

scvi-tools relies entirely on the AnnData format. For convenience, we have included data loaders from the AnnData API. Scanpy also has utilities to load data that are outputted by 10x’s Cell Ranger software.

data.read_h5ad	Read .h5ad-formatted hdf5 file.
data.read_csv	Read .csv file.
data.read_loom	Read .loom-formatted hdf5 file.
data.read_text	Read .txt, .tab, .data (text) file.
data.read_10x_atac	Read scATAC-seq data outputted by 10x Genomics software.
data.read_10x_multiome	Read Multiome (scRNA + scATAC) data outputted by 10x Genomics software.

Basic preprocessing

For general single-cell preprocessing, we defer to our friends at Scanpy, and specifically their preprocessing module (scanpy.pp).

All scvi-tools models require raw UMI count data. The count data can be safely stored in an AnnData layer as one of the first steps of a Scanpy single-cell workflow:

adata.layers["counts"] = adata.X.copy()

Here we maintain a few package specific utilities for feature selection, etc.

data.poisson_gene_selection	Rank and select genes based on the enrichment of zero counts.
data.organize_cite_seq_10x	Organize anndata object loaded from 10x for scvi models.
data.organize_multiome_anndatas	Concatenate multiome and single-modality input anndata objects.
data.add_dna_sequence	Add the DNA sequence to AnnData object.
data.reads_to_fragments	Convert scATAC-seq read counts to approximate fragment counts.

Model hyperparameter tuning

scvi-tools supports automatic model hyperparameter tuning using Ray Tune.

autotune.run_autotune	Run a hyperparameter sweep.
autotune.AutotuneExperiment	Track hyperparameter tuning experiments.

Training configuration

train.TrainingPlanConfig	Config for TrainingPlan.
train.AdversarialTrainingPlanConfig	Config for AdversarialTrainingPlan.
train.SemiSupervisedTrainingPlanConfig	Config for SemiSupervisedTrainingPlan.
train.SemiSupervisedAdversarialTrainingPlanConfig	Config for SemiSupervisedAdversarialTrainingPlan.
train.PyroTrainingPlanConfig	Config for PyroTrainingPlan.
train.LowLevelPyroTrainingPlanConfig	Config for LowLevelPyroTrainingPlan.
train.ClassifierTrainingPlanConfig	Config for ClassifierTrainingPlan.
train.JaxTrainingPlanConfig	Config for JaxTrainingPlan.
train.TrainerConfig	Config for Trainer.

Model hub

We have a hub for pre-trained scvi-tools models that is hosted on huggingface. Using the functionality that scvi-tools provides, users can download pre-trained scvi-tools models (and datasets) from this platform, and model generators can upload their own pre-trained scvi-tools models to this platform.

hub.HubMetadata	Encapsulates the required metadata for scvi-tools hub models.
hub.HubModelCardHelper	A helper for creating a ModelCard for scvi-tools hub models.
hub.HubModel	Wrapper for BaseModelClass backed by HuggingFace Hub.

Model criticism

criticism.PosteriorPredictiveCheck

Posterior predictive checks for comparing scRNA-seq generative models.

Utilities

Here we maintain miscellaneous general methods.

model.utils.get_minified_adata_scrna

Returns a minified AnnData.

Configuration

An instance of the ScviConfig is available as scvi.settings and allows configuring scvi-tools.

_settings.ScviConfig

Config manager for scvi-tools.