Temporal GeneTerrain: advancing precision medicine through dynamic gene expression visualization
Ehsan Saghapour1,2, Rahul Sharma1, Delower Hossain2,3
1Department of Biomedical Informatics and Data Science, The University of Alabama at Birmingham, Birmingham, AL, United States.
Introduction:
Understanding the temporal dynamics of gene expression is vital for interpreting biological responses, especially in drug treatment studies. Conventional visualization techniques, such as heatmaps and static clustering, often fail to effectively capture these temporal dynamics, particularly when analyzing large-scale multidimensional datasets. These traditional methods tend to obscure fine-grained temporal transitions, resulting in overcrowded visualizations, diminished clarity, and limited interpretability of biologically significant patterns.
Methods:
To address these visualization challenges, we introduce Temporal GeneTerrain, an advanced method designed to represent dynamic changes in gene expression over time. We applied Temporal GeneTerrain to compare transcriptomic perturbations induced by mefloquine (M), tamoxifen (T), and withaferin A (W), both individually and in all-pairwise and triple combinations (TM, TW, MW, and TMW), in LNCaP prostate cancer cells using the GSE149428 dataset (0, 3, 6, 9, 12, and 24 h). Expression values were first Z-score normalized, and the 1,000 most variably expressed genes were selected. To ensure coordinated temporal dynamics, we calculated Pearson correlation coefficients among these genes and retained those with r ≥ 0.5, resulting in 999 strongly co-expressed candidates. We then constructed a protein-protein interaction network for these genes and embedded it in two dimensions using the Kamada-Kawai force-directed algorithm. Finally, for each time point and treatment, we mapped the normalized expression values of the corresponding genes onto the fixed Kamada-Kawai layout as Gaussian density fields (σ = 0.03), generating a distinct Temporal GeneTerrain map for each time-condition combination.
Results:
The application of Temporal GeneTerrain revealed intricate temporal shifts in gene expression, particularly unveiling delayed responses in pathways such as NGF-stimulated transcription and the unfolded protein response under combined drug treatments. Compared to traditional heatmap visualizations, Temporal GeneTerrain significantly improved both resolution and interpretability, effectively capturing gene expression patterns' multidimensional and transient nature. This enhancement provides a solid foundation for further research and analysis, assuring the scientific community of the method's reliability.
Discussion:
Temporal GeneTerrain addresses the limitations of traditional visualization methods by offering an intuitive and detailed representation of gene expression dynamics. Compared to other approaches, such as heatmaps and static clustering, Temporal GeneTerrain uniquely captures the transient nature of gene expression patterns. This method significantly enhances the interpretability of complex biological datasets, thereby supporting informed decision-making in biological research and therapeutic development.
