Comprehensive and critical evaluation of individualized pathway activity measurement tools on pan-cancer data

Biological pathways are extensively used for the analysis of transcriptome data to characterize biological mechanisms underlying various phenotypes. There are a number of computational tools that summarize transcriptome data at the pathway level. However, there is no comparative study on how well these tools produce useful information at the cohort level, enabling comparison of many samples or patients.

In this study, we systematically compared and evaluated 13 different pathway activity inference tools based on 5 comparison criteria using pan-cancer data set. This study has two major contributions. First, our study provides a comprehensive survey on computational techniques used by existing pathway activity inference tools. The tools use different strategies and assume different requirements on data: input transformation, use of labels, necessity of cohort-level input data, use of gene relations and scoring metric. Second, we performed extensive evaluations on the performance of these tools. Because different tools use different methods to map samples to the pathway dimension, the tools are evaluated at the pathway level using five comparison criteria. Starting from measuring how well a tool maintains the characteristics of original gene expression values, robustness was also investigated by adding noise into gene expression data. Classification tasks on three clinical variables (tumor versus normal, survival and cancer subtypes) were performed to evaluate the utility of tools for their clinical applications. In addition, the inferred activity values were compared between the tools to see how similar they are along with the scoring schemes they use.