scVI

single-cell Variational Inference (scVI) is a generative model for scRNA-seq data.

Publication

Romain Lopez, Jeffrey Regier, Michael Cole, Michael I. Jordan, Nir Yosef. “Deep generative modeling for single-cell transcriptomics” Nature Methods, 2018.

Ressources

Paper: https://www.nature.com/articles/s41592-018-0229-2
Codebase: https://github.com/YosefLab/scVI
Blog post: https://bair.berkeley.edu/blog/2018/12/05/genes/

Abstract

Single-cell transcriptome measurements can reveal unexplored biological diversity, but they suffer from technical noise and bias that must be modeled to account for the resulting uncertainty in downstream analyses. Here we introduce single-cell variational inference (scVI), a ready-to-use scalable framework for the probabilistic representation and analysis of gene expression in single cells (https://github.com/YosefLab/scVI). scVI uses stochastic optimization and deep neural networks to aggregate information across similar cells and genes and to approximate the distributions that underlie observed expression values, while accounting for batch effects and limited sensitivity. We used scVI for a range of fundamental analysis tasks including batch correction, visualization, clustering, and differential expression, and achieved high accuracy for each task.

scvi

Figure: An artistic representation of single-cell RNA sequencing. The stars in the sky represent cells in a heterogeneous tissue. The projection of the stars onto the river reveals relationships among them that are not apparent by looking directly at the sky. Like the river, our Bayesian model, called scVI, reveals relationships among cells.