User guide

scvi-tools is composed of models that can perform one or many analysis tasks. In the user guide, we provide an overview of each model with emphasis on the math behind the model, how it connects to the code, and how the code connects to analysis.

Overview of tasks

scRNA-seq analysis

Model

Reference

Tasks

scVI

[Lopez18]

Dimensionality reduction, removal of unwanted variation, integration across replicates, donors, and technologies, differential expression, imputation, normalization of other cell- and sample-level confounding factors

scANVI

[Xu21]

scVI tasks with cell type transfer from reference, seed labeling

LDVAE

[Svensson20]

scVI tasks with linear decoder

AUTOZI

[Clivio19]

for assessing gene-specific levels of zero-inflation in scRNA-seq data

CellAssign

[Zhang19]

Marker-based automated annotation

Solo

[Bernstein19]

Doublet detection

ATAC-seq analysis

Model

Reference

Tasks

PeakVI

[Ashuach21]

Dimensionality reduction, removal of unwanted variation, integration across replicates, donors, and technologies, differential expression, imputation, normalization of other cell- and sample-level confounding factors

Multimodal analysis

CITE-seq

Model

Reference

Tasks

totalVI

[GayosoSteier21]

Dimensionality reduction, removal of unwanted variation, integration across replicates, donors, and technologies, differential expression, protein imputation, imputation, normalization of other cell- and sample-level confounding factors

Multiome

Model

Reference

Tasks

MultiVI

[AshuachGabitto21]

Integration of paired/unpaired multiome data, missing modality imputation, normalization of other cell- and sample-level confounding factors

Spatial transcriptomics analysis

Model

Reference

Tasks

DestVI

[Lopez21]

Multi-resolution deconvolution, cell-type-specific gene expression imputation, comparative analysis

Stereoscope

[Andersson20]

Deconvolution

gimVI

[Lopez19]

Imputation of missing spatial genes

General purpose analysis

Model

Reference

Tasks

Amortized LDA

[Blei03]

Topic modeling