Tutorials

The easiest way to get familiar with scvi-tools is to follow along with our tutorials. Many are also designed to work seamlessly in Google Colab, a free cloud computing platform. Tutorials by default work with the latest installable version of scvi-tools. To view older tutorials, change the documentation version using the tab at the bottom of the left sidebar.

Note

For questions about using scvi-tools, or broader questions about modeling data, please use our forum. Checkout the ecosystem for additional models powered by scvi-tools.

• Quick start
  • Introduction to scvi-tools
  • Data loading and preparation
  • Using Python in R with reticulate
• scRNA-seq
  • Atlas-level integration of lung data
  • Integrating datasets with scVI in R
  • Integration and label transfer with Tabula Muris
  • Reference mapping with scvi-tools
  • Querying the Human Lung Cell Atlas
  • Seed labeling with scANVI
  • Linearly decoded VAE
  • Identification of zero-inflated genes
  • Annotation with CellAssign
  • Topic Modeling with Amortized LDA
  • Differential expression on C. elegans data
  • Isolating perturbation-induced variations with contrastiveVI
  • Benchmarking the scANVI fix
  • MrVI Quick Start Tutorial
  • Integration of scRNA-seq data with substantial batch effects using sysVI
  • Decipher Quick Start Tutorial
  • Variational inference for RNA velocity with VeloVI
• ATAC-seq
  • PeakVI: Analyzing scATACseq data
  • ATAC-seq analysis in R
  • ScBasset: Analyzing scATACseq data
  • scBasset: Batch correction of scATACseq data
  • PoissonVI: Analyzing quantitative scATAC-seq fragment counts
• scBS-seq
  • Integrating single-cell methylation data from different scBS-seq experiments with methylVI
• Multimodal
  • CITE-seq analysis with totalVI
  • Integration of CITE-seq and scRNA-seq data
  • CITE-seq reference mapping with totalVI
  • CITE-seq analysis in R
  • Joint analysis of paired and unpaired multiomic data with MultiVI
• Spatial transcriptomics
  • ResolVI to address noise and biases in spatial transcriptomics
  • Multi-resolution deconvolution of spatial transcriptomics
  • Multi-resolution deconvolution of spatial transcriptomics in R
  • Introduction to gimVI
  • Spatial mapping with Tangram
  • Stereoscope applied to left ventricule data
  • Mapping human lymph node cell types to 10X Visium with Cell2location
• Model hub
  • Use pretrained models of scVI-hub for CELLxGENE
  • Using scvi-hub to download pretrained scvi-tools models
  • Using scvi-hub to upload pretrained scvi-tools models
• Common Modelling Use Cases
  • Model hyperparameter tuning with scVI
  • Minification
  • Using SHAP values and IntegratedGradients for cell type classification interpretability
• Development
  • Data handling in scvi-tools
  • Constructing a probabilistic module
  • Constructing a high-level model