Differential Gene Expression Analysis with Visual Analytics

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dc.contributor.authorFortunato, Francescoen_US
dc.contributor.authorSantaroni, Cristianen_US
dc.contributor.authorBlasilli, Grazianoen_US
dc.contributor.authorFiscon, Giuliaen_US
dc.contributor.authorLenti, Simoneen_US
dc.contributor.authorSantucci, Giuseppeen_US
dc.contributor.editorDiehl, Alexandraen_US
dc.contributor.editorKucher, Kostiantynen_US
dc.contributor.editorMédoc, Nicolasen_US
dc.date.accessioned2025-05-26T06:54:49Z
dc.date.available2025-05-26T06:54:49Z
dc.date.issued2025
dc.description.abstractDifferential 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.en_US
dc.description.sectionheadersPosters
dc.description.seriesinformationEuroVis 2025 - Posters
dc.identifier.doi10.2312/evp.20251126
dc.identifier.isbn978-3-03868-286-8
dc.identifier.pages3 pages
dc.identifier.urihttps://doi.org/10.2312/evp.20251126
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/evp20251126
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing → Visual analytics
dc.subjectHuman centered computing → Visual analytics
dc.titleDifferential Gene Expression Analysis with Visual Analyticsen_US
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