Benchmarking algorithms for pathway activity transformation of single-cell RNA-seq data

Yaru Zhang; Yunlong Ma; Yukuan Huang; Yan Zhang; Qi Jiang; Meng Zhou; Jianzhong Su

doi:10.1016/j.csbj.2020.10.007

Benchmarking algorithms for pathway activity transformation of single-cell RNA-seq data

Comput Struct Biotechnol J. 2020 Oct 15:18:2953-2961. doi: 10.1016/j.csbj.2020.10.007. eCollection 2020.

Authors

Yaru Zhang¹, Yunlong Ma¹, Yukuan Huang¹, Yan Zhang¹, Qi Jiang¹, Meng Zhou¹, Jianzhong Su^{1

2}

Affiliations

¹ Institute of Biomedical Big Data, School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China.
² Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325011, China.

Abstract

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.

Keywords: Benchmark; Gene expression; Pathway analysis; scRNA-seq.