From RNA sequencing measurements to the final results: a practical guide to navigating the choices and uncertainties of gene set analysis

TL;DR

This paper provides a comprehensive, practical guide for gene set analysis from RNA sequencing data, emphasizing preprocessing choices, method selection, and uncertainties to improve reproducibility and transparency.

Contribution

It offers a detailed overview of gene set analysis methods, focusing on preprocessing steps and uncertainties, with illustrative R code and practical recommendations.

Findings

Highlights importance of data preprocessing choices

Provides illustrative R code for analysis pipelines

Discusses uncertainties and best practices in gene set analysis

Abstract

Gene set analysis, a popular approach for analyzing high-throughput gene expression data, aims to identify sets of related genes that show significantly enriched or depleted expression patterns between different conditions. In the last years, a multitude of methods and corresponding tools have been developed for this task. However, clear guidance is lacking: choosing the right method is the first hurdle a researcher is confronted with. No less challenging than overcoming this so-called method uncertainty is the procedure of preprocessing, from knowing which steps are required to selecting a corresponding approach from the plethora of valid options to create the accepted input object (data preprocessing uncertainty), with clear guidance again being scarce. Here, we provide a practical guide through all steps required to conduct gene set analysis, beginning with a concise overview of a selection of established methods, including GSEA and DAVID. We thereby lay a special focus on reviewing and explaining the necessary preprocessing steps for each method under consideration (e.g. the necessity of a transformation of the RNA-Seq data)-an essential aspect that is typically paid only limited attention to in both existing reviews and applications. To raise awareness of the spectrum of uncertainties, our review is accompanied by an extensive overview of the literature on valid approaches for each step and illustrative R code demonstrating the complex analysis pipelines. It ends with a discussion and recommendations to both users and developers to ensure that the results of gene set analysis are, despite the above-mentioned uncertainties, replicable and transparent.