Background The rapid development of single-cell RNA-sequencing (scRNA-seq) technologies has led to the emergence of many methods for removing systematic technical noises, including imputation methods, which aim to address the increased sparsity observed in single-cell data. Although many imputation methods have been developed, there is no consensus on how methods compare to each other. Results Here, we perform a systematic evaluation of 18 scRNA-seq imputation methods to assess their accuracy and usability. We benchmark these methods in terms of the similarity between imputed cell profiles and bulk samples and whether these methods recover relevant biological signals or introduce spurious noise in downstream differential expression, unsupervised clustering, and pseudotemporal trajectory analyses, as well as their computational run time, memory usage, and scalability. Methods are evaluated using data from both cell lines and tissues and from both plate- and droplet-based single-cell platforms. Conclusions We found that the majority of scRNA-seq imputation methods outperformed no imputation in recovering gene expression observed in bulk RNA-seq. However, the majority of the methods did not improve performance in downstream analyses compared to no imputation, in particular for clustering and trajectory analysis, and thus should be used with caution. In addition, we found substantial variability in the performance of the methods within each evaluation aspect. Overall, MAGIC, kNN-smoothing, and SAVER were found to outperform the other methods most consistently.

中文翻译：

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单细胞 RNA 测序插补方法的系统评估

背景 单细胞 RNA 测序 (scRNA-seq) 技术的快速发展导致出现了许多消除系统技术噪声的方法，包括插补方法，旨在解决单细胞数据中观察到的稀疏性增加问题。尽管已经开发了许多插补方法，但对于如何相互比较这些方法尚未达成共识。结果在这里，我们对 18 种 scRNA-seq 插补方法进行了系统评估，以评估其准确性和可用性。我们根据估算细胞概况和批量样本之间的相似性以及这些方法是否恢复相关生物信号或在下游差异表达、无监督聚类和伪时间轨迹分析中引入虚假噪声，以及它们的计算运行时间、内存来对这些方法进行基准测试。使用情况和可扩展性。使用来自细胞系和组织以及基于板和液滴的单细胞平台的数据来评估方法。结论 我们发现，大多数 scRNA-seq 插补方法在恢复批量 RNA-seq 中观察到的基因表达方面优于无插补方法。然而，与无插补相比，大多数方法并没有提高下游分析的性能，特别是对于聚类和轨迹分析，因此应谨慎使用。此外，我们发现每个评估方面的方法性能存在很大差异。总体而言，MAGIC、kNN-smoothing 和 SAVER 的表现始终优于其他方法。