Single-cell RNA-sequencing (scRNA-seq) is a recent breakthrough technology, which paves the way for measuring RNA levels at single cell resolution to study precise biological functions. One of the main challenges when analyzing scRNA-seq data is the presence of zeros or dropout events, which may mislead downstream analyses. To compensate the dropout effect, several methods have been developed to impute gene expression since the first Bayesian-based method being proposed in 2016. However, these methods have shown very diverse characteristics in terms of model hypothesis and imputation performance. Thus, largescale comparison and evaluation of these methods is urgently needed now. To this end, we compared eight imputation methods, evaluated their power in recovering original real data, and performed broad analyses to explore their effects on clustering cell types, detecting differentially expressed genes, and reconstructing lineage trajectories in the context of both simulated and real data. Simulated datasets and case studies highlight that there are no one method performs the best in all the situations. Some defects of these methods such as scalability, robustness and unavailability in some situations need to be addressed in future studies.

中文翻译：

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估算单细胞RNA测序数据的计算方法的比较。

单细胞RNA测序（scRNA-seq）是一项最新的突破性技术，它为以单细胞分辨率测量RNA水平铺平了道路，从而研究了精确的生物学功能。分析scRNA-seq数据时的主要挑战之一是零或丢失事件的存在，这可能会误导下游分析。为了弥补丢失的影响，自2016年首次提出基于贝叶斯的方法以来，已经开发了几种估算基因表达的方法。但是，这些方法在模型假设和估算性能方面表现出非常不同的特征。因此，现在迫切需要对这些方法进行大规模的比较和评估。为此，我们比较了八种插补方法，评估了它们在恢复原始真实数据中的作用，并进行了广泛的分析，以探讨它们对聚类细胞类型的影响，检测差异表达的基因以及在模拟和真实数据的背景下重建谱系轨迹。模拟数据集和案例研究表明，没有一种方法在所有情况下都能发挥最佳性能。这些方法的某些缺陷，例如在某些情况下的可伸缩性，鲁棒性和不可用性，需要在以后的研究中加以解决。