Deep generative modeling for single-cell transcriptomics

[evancofer]

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.

https://doi.org/10.1038/s41592-018-0229-2

[evancofer]

Associated comment piece: https://doi.org/10.1038/s41592-018-0230-9

[agitter]

Cross-referencing the scVI preprint #682 that we cited. This whole paragraph in the review could use updating, there has been a lot new work in the area:

Neural networks can also learn low-dimensional representations of single-cell gene expression data for visualization, clustering, and other tasks...