Saghapour Ehsan, Sharma Rahul, Hossain Delower, Song Kevin, Sembay Zhandos, Chen Jake Y
Department of Biomedical Informatics and Data Science, The University of Alabama at Birmingham, Birmingham, AL, United States.
Systems Pharmacology AI Research Center, The University of Alabama at Birmingham, Birmingham, AL, United States.
Front Bioinform. 2025 Jun 18;5:1602850. doi: 10.3389/fbinf.2025.1602850. eCollection 2025.
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.
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.
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.
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.
了解基因表达的时间动态对于解释生物学反应至关重要，尤其是在药物治疗研究中。传统的可视化技术，如图热图和静态聚类，往往无法有效捕捉这些时间动态，特别是在分析大规模多维数据集时。这些传统方法往往会掩盖细粒度的时间转变，导致可视化过于拥挤、清晰度降低以及生物学上显著模式的可解释性受限。
为应对这些可视化挑战，我们引入了Temporal GeneTerrain，这是一种旨在表示基因表达随时间动态变化的先进方法。我们应用Temporal GeneTerrain，使用GSE149428数据集（0、3、6、9、12和24小时），比较了甲氟喹（M）、他莫昔芬（T）和Withaferin A（W）单独以及所有成对和三联组合（TM、TW、MW和TMW）在LNCaP前列腺癌细胞中诱导的转录组扰动。首先对表达值进行Z分数标准化，并选择1000个表达变化最大的基因。为确保协调的时间动态，我们计算了这些基因之间的皮尔逊相关系数，并保留r≥0.5的基因，从而得到999个强共表达候选基因。然后，我们为这些基因构建了一个蛋白质 - 蛋白质相互作用网络，并使用Kamada - Kawai力导向算法将其嵌入二维空间。最后，对于每个时间点和处理，我们将相应基因的标准化表达值作为高斯密度场（σ = 0.03）映射到固定的Kamada - Kawai布局上，为每个时间 - 条件组合生成一个独特的Temporal GeneTerrain图。
Temporal GeneTerrain的应用揭示了基因表达中复杂的时间变化，特别是揭示了联合药物治疗下NGF刺激转录和未折叠蛋白反应等途径中的延迟反应。与传统的热图可视化相比，Temporal GeneTerrain显著提高了分辨率和可解释性，有效捕捉了基因表达模式的多维和瞬态性质。这种增强为进一步的研究和分析提供了坚实的基础，确保了科学界对该方法可靠性的认可。
Temporal GeneTerrain通过提供基因表达动态的直观和详细表示，解决了传统可视化方法的局限性。与其他方法（如图热图和静态聚类）相比，Temporal GeneTerrain独特地捕捉了基因表达模式的瞬态性质。该方法显著提高了复杂生物学数据集的可解释性，从而支持生物学研究和治疗开发中的明智决策。
