Transcriptomic Data Visualization Frameworks

Abstract

Transcriptomic experiments generate high-dimensional expression matrices spanning thousands of genes across hundreds of samples, and effective visualization frameworks that transform these matrices into interpretable graphical representations are essential for quality assessment, pattern discovery, and biological communication, yet adoption varies across framework categories and user communities. We evaluated 214 transcriptomic visualization programmes across centres in Estonia and Spain between 2018 and 2022, spanning five framework categories: static publication-quality plot libraries, interactive web-based exploration dashboards, single-cell embedding and annotation viewers, integrated analysis-visualization platforms, and automated report generation systems. A Transcriptomic Visualization Framework Index (TVFI) was constructed from five sub-scores -- visual clarity and information density, interactivity and exploration depth, scalability to large datasets, customisation flexibility, and learning curve accessibility - with weights from regression against sustained adoption into transcriptomics workflows. TVFI correlated with adoption at r = +0.84 and discriminated adopted from non-adopted frameworks with an AUC of 0.882. Interactive dashboards scored highest (mean TVFI 0.824), while automated reports trailed at 0.598. Only 35.5 percent exceeded the 0.75 threshold. Visual clarity carried the largest weight (beta = +0.278), followed by interactivity (beta = +0.230).