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. Researchers from Wenzhou Medical University collected seven widely-used pathway activity transformation algorithms and 32 available datasets based on 16 scRNA-seq techniques. They 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. The researchers 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.
An evaluation framework for benchmarking pathway activity score (PAS) calculators
The seven widely applied PAS inference algorithms were assessed on 32 well-defined benchmark data sets. These algorithms combined prior knowledge (biological pathways or functional gene sets) with a statistic method to aggregate gene-level matrix into PAS-level matrix. The accuracy (take into account three downstream applications), stability of results (in the presence of dropout events and across technologies), and scalability (running time and memory usage) were used to systematically evaluate these algorithms.