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