GEVIS: A Workflow-Driven Visual Analytics Approach to Differential Gene Expression Analysis

Differential gene expression (DGE) analysis is one of the most widely used techniques for investigating RNA-seq data and supports numerous medical and biological applications, including biomarker identification for diagnosis and prognosis, as well as the evaluation of medical treatments. However, performing DGE analysis typically requires navigating a complex multistep pipeline and proficiency in programming languages such as R. This poses a barrier for researchers --- including biologists and clinicians --- who may lack coding expertise, and adds overhead for experienced bioinformaticians. To address these challenges, we propose a workflow-driven visual analytics approach for DGE analysis that integrates state-of-the-art methodologies and supports interactive exploration of gene expression data through a guided step-by-step process. Building on this workflow, we developed GEVIS, a visual analytics system that enables users to conduct DGE analysis without writing code, thereby reducing analytical overhead and making the process more accessible to a broader audience. Both the workflow and the GEVIS system have been validated by experts in bioinformatics and demonstrated through a use case.