Fortunato, FrancescoSantaroni, CristianBlasilli, GrazianoFiscon, GiuliaLenti, SimoneSantucci, GiuseppeDiehl, AlexandraKucher, KostiantynMédoc, Nicolas2025-05-262025-05-262025978-3-03868-286-8https://doi.org/10.2312/evp.20251126https://diglib.eg.org/handle/10.2312/evp20251126Differential gene expression (DGE) analysis is one of the most used techniques for RNA-seq data analysis, and it is applied in various medical and biological contexts, including biomarkers for diagnosis and prognosis and evaluation of the effectiveness of specific treatments. The conduction of a DGE analysis typically involves navigating a complex, multi-step pipeline, which usually requires proficiency in programming languages like R. This presents a barrier to researchers like biologists and clinicians, who may have limited or no coding skills, and adds additional overhead even for experienced bioinformaticians. To overcome these challenges, we propose a preliminary visual analytics prototype that simplifies DGE analysis, enabling users to perform the analyses without coding expertise.Attribution 4.0 International LicenseCCS Concepts: Human-centered computing → Visual analyticsHuman centered computing → Visual analytics10.2312/evp.202511263 pages