Temporal GeneTerrain: Advancing Precision Medicine Through Dynamic Gene Expression Visualization

Ehsan SaghapourRahul SharmaDelower HossainKevin SongZhandos SembayJake Y Chen^*

• University of Alabama at Birmingham, Birmingham, United States

Temporal dynamics of gene expression are critically important for understanding biological responses, particularly in drug treatment studies. However, traditional visualization techniques, such as heatmaps and static clustering methods, often inadequately capture these dynamics, especially when analyzing large-scale multidimensional datasets. These methods typically struggle to depict temporal transitions, resulting in overcrowded visuals, reduced resolution, and limited interpretability of biologically meaningful patterns. To overcome these limitations, we introduce the Temporal GeneTerrain, an advanced visualization approach that captures and clarifies dynamic gene expression changes over time. We applied Temporal GeneTerrains to the GSE149428 dataset, which examines the effects of individual and combined treatments of mefloquine, tamoxifen, and withaferin A on prostate cancer (LNCaP) cell lines across multiple time points (0, 3, 6, 9, and 12 hours). Our analysis revealed detailed temporal shifts in gene expression, particularly highlighting delayed responses in pathways such as NGF-stimulated transcription and unfolded protein response under combined drug treatments. Compared to traditional heatmap representations, Temporal GeneTerrains offer enhanced resolution and interpretability, effectively visualizing the multidimensional and transitional nature of gene expression data. Furthermore, integrating gene set enrichment analysis via tools such as PAGER allowed us to directly associate observed expression changes with critically important biological pathways and mechanisms, further validating the biological relevance of uncovered temporal patterns. Although conventional visualization techniques often suffer from data overcrowding and reduced clarity, Temporal GeneTerrains provide an intuitive, comprehensive, and insightful solution. This approach facilitates our understanding of cells' temporal gene expression dynamics and supports informed decision-making in biological research and therapeutic development.