Deep Generative Models for Single-cell Transcriptomics
Single-cell Variational Inference (scVI) is a set of tools for probabilistic analysis of single-cell RNA-sequencing data. We propose to solve quantitative biological questions with tailored and scalable Bayesian machinery. Examples of biological questions include cell-type discovery, data integration, transfer of annotations and differential expression analysis. Examples of statistical tools include deep generative models, stochastic variational inference, Bayesian decision theory and domain adaptation.
All the models in our codebase may fit millions of transcriptomic measurements in a few hours. In addition to the original publication, we recommend the following ressources to get more familiar with scVI: