The high proportion of zeros in typical single-cell RNA sequencing datasets has led to widespread but inconsistent use of terminology such as dropout and missing data. Here, we argue that much of this terminology is unhelpful and confusing, and outline simple ideas to help to reduce confusion. These include: (1) observed single-cell RNA sequencing counts reflect both true gene expression levels and measurement error, and carefully distinguishing between these contributions helps to clarify thinking; and (2) method development should start with a Poisson measurement model, rather than more complex models, because it is simple and generally consistent with existing data. We outline how several existing methods can be viewed within this framework and highlight how these methods differ in their assumptions about expression variation. We also illustrate how our perspective helps to address questions of biological interest, such as whether messenger RNA expression levels are multimodal among cells.

典型的单细胞 RNA 测序数据集中零所占的比例很高，导致了诸如丢失和缺失数据等术语的广泛但不一致的使用。在这里，我们认为这些术语中的大部分都是无用且令人困惑的，并概述了简单的想法以帮助减少混乱。其中包括：（1）观察到的单细胞RNA测序计数既反映了真实的基因表达水平，也反映了测量误差，仔细区分这些贡献有助于理清思路； (2)方法开发应该从泊松测量模型开始，而不是更复杂的模型，因为它很简单并且通常与现有数据一致。我们概述了如何在此框架内查看几种现有方法，并强调这些方法在表达变异假设方面有何不同。我们还说明了我们的观点如何帮助解决生物学感兴趣的问题，例如信使 RNA 表达水平在细胞之间是否是多模式的。