Single-cell sequencing technology holds the promise of unravelling cell heterogeneities hidden in ubiquitous bulk-level analyses. However, limitations of current experimental methods 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 talk, we will focus on probabilistic models designed for this task. First, we will overview the difficulties associated with the data. Then, we look at some of the generative models designed by researchers to disentangle the assumed factors of variation underlying the observations. Finally, we overview variational inference and how it helps in making these models useful for analysis.