With the development of single-cell RNA sequencing technology (scRNA-seq), we have the ability to study biological questions at the level of the individual cell transcriptome. Nowadays, many analysis tools, specifically suitable for single-cell RNA sequencing data, have been developed. In this review, the currently commonly used scRNA-seq protocols are discussed. The upstream processing flow pipeline of scRNA-seq data, including goals and popular tools for reads mapping and expression quantification, quality control, normalization, imputation, and batch effect removal is also introduced. Finally, methods to evaluate these tools in both cellular and genetic dimensions, clustering and differential expression analysis are presented.

中文翻译：

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提高分析性能的scRNA-seq数据分析方法。

随着单细胞RNA测序技术（scRNA-seq）的发展，我们有能力在单个细胞转录组水平上研究生物学问题。如今，已经开发出许多专门适用于单细胞RNA测序数据的分析工具。在这篇综述中，讨论了目前常用的 scRNA-seq 协议。还介绍了 scRNA-seq 数据的上游处理流程，包括读取映射和表达量化、质量控制、归一化、插补和批量效应去除的目标和流行工具。最后，介绍了在细胞和遗传维度、聚类和差异表达分析方面评估这些工具的方法。