We propose a probabilistic model for interpreting gene expression levels that
are observed through single-cell RNA sequencing. In the model, each cell has a
low-dimensional latent representation. Additional latent variables account for
technical effects that may erroneously set some observations of gene expression
levels to zero. Conditional distributions are specified by neural networks,
giving the proposed model enough flexibility to fit the data well. We use
variational inference and stochastic optimization to approximate the posterior
distribution. The inference procedure scales to over one million cells, whereas
competing algorithms do not. Even for smaller datasets, for several tasks, the
proposed procedure outperforms state-of-the-art methods like ZIFA and
ZINB-WaVE. We also extend our framework to account for batch effects and other
confounding factors, and propose a Bayesian hypothesis test for differential
expression that outperforms DESeq2.

提出了一种针对单细胞 RNA 测序随机解释基因表达水平的概率模型，其利用低维潜在表示、额外潜在变量和神经网络进行条件分布的建模，并利用方差推断和随机优化来拟合数据。此推断过程适用于 100 万个细胞以上的数据，且优于 ZIFA 和 ZINB-WaVE 方法。作者还将该框架扩展到批次效应和其他混淆因素，并提出了一种超越 DESeq2 方法的 Bayesian 假设检验，以分析基因差异表达。