Biological pathway analysis provides new insights for cell clustering and functional annotation from single-cell RNA sequencing (scRNA-seq) data. Many pathway analysis algorithms have been developed to transform gene-level scRNA-seq data into functional gene sets representing pathways or biological processes. Here, we collected seven widely-used pathway activity transformation algorithms and 32 available datasets based on 16 scRNA-seq techniques. We proposed a comprehensive framework to evaluate their accuracy, stability and scalability. The assessment of scRNA-seq preprocessing showed that cell filtering had the less impact on scRNA-seq pathway analysis, while data normalization of sctransform and scran had a consistent well impact across all tools. We found that Pagoda2 yielded the best overall performance with the highest accuracy, scalability, and stability. Meanwhile, the tool PLAGE exhibited the highest stability, as well as moderate accuracy and scalability.

生物通路分析为单细胞 RNA 测序 (scRNA-seq) 数据的细胞聚类和功能注释提供了新的见解。已经开发了许多通路分析算法来将基因级 scRNA-seq 数据转换为代表通路或生物过程的功能基因集。在这里，我们收集了 7 种广泛使用的通路活性转换算法和基于 16 种 scRNA-seq 技术的 32 个可用数据集。我们提出了一个全面的框架来评估其准确性、稳定性和可扩展性。scRNA-seq 预处理的评估表明，细胞过滤对 scRNA-seq 通路分析的影响较小，而 sctransform 和 scran 的数据标准化在所有工具中具有一致的良好影响。我们发现 Pagoda2 具有最佳的整体性能，具有最高的准确性、可扩展性和稳定性。同时，PLAGE工具表现出最高的稳定性，以及中等的准确性和可扩展性。