Variational Inference in Probabilistic Single-cell RNA-seq Models

摘要

Single-cell sequencing technology holds the promise of unravelling cell heterogeneities hidden in ubiquitous bulk-level analyses. However, limitations of current experimental methods also pose new obstacles that prevent accurate conclusions from being drawn. To overcome this, researchers have developed computational methods which aim at extracting the biological signal of interest from the noisy observations. In this paper we focus on probabilistic models designed for this task. Particularly, we describe how variational inference constitutes a powerful inference mechanism for different sample sizes, and critically review two recent scRNA-seq models which use it.