Comparison of single sample Gene Set Enrichment Analysis methods for functional analysis in research and diagnostics

Transcriptomics arrived with the prospect of mechanistic deduction and quantification of active molecular pathways, providing insights into both state and function of cells. However, it remains a challenging task to interpret functional analysis of single samples of RNA-sequencing data, because of inconsistencies between methods, and inherent reliance on a priori gene signatures encoding known biological knowledge. Initially, methods were designed for experimental setups with known conditions and biological replicates, but for many use-cases, particularly in clinical diagnostics, the problem is N-of-1, where each single sample presents a unique and independent case, without replicates or baseline for comparison. Here, we are comparing the performance of 17 single-sample GSEA methods on a diverse set of datasets, with known biology, and test them on a matched collection of gene signatures, with relevance to both experimental research and clinical diagnostics. We find that truly single-sample methods perform well and that the methods that we adapted to work on individual samples, by introducing a large data collection as a universal reference, are generally able to correctly classify different samples of known phenotypes. Single-sample GSEA remains a challenging task where the tool, query data, gene signature, and potential baseline influence the results. Here, we have systematically assessed a range of tools using a designed truth set of data and gene sets. Z-score-based and truly single-sample rank-based methods were consistently competitive, but method choice must be validated for each dataset–gene set context. The benchmark is implemented in the OmniBenchmark framework, inviting continuous additions of relevant tools and datasets.