Temporal GeneTerrain: advancing precision medicine through dynamic gene expression visualization
IntroductionUnderstanding 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, particula...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-06-01
|
| Series: | Frontiers in Bioinformatics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fbinf.2025.1602850/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850117901019774976 |
|---|---|
| author | Ehsan Saghapour Ehsan Saghapour Rahul Sharma Delower Hossain Delower Hossain Kevin Song Kevin Song Zhandos Sembay Zhandos Sembay Jake Y. Chen Jake Y. Chen |
| author_facet | Ehsan Saghapour Ehsan Saghapour Rahul Sharma Delower Hossain Delower Hossain Kevin Song Kevin Song Zhandos Sembay Zhandos Sembay Jake Y. Chen Jake Y. Chen |
| author_sort | Ehsan Saghapour |
| collection | DOAJ |
| description | IntroductionUnderstanding 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.MethodsTo 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.ResultsThe 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.DiscussionTemporal 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. |
| format | Article |
| id | doaj-art-2c3ffc36ca9845b9a3b1e955fbbfc2a2 |
| institution | OA Journals |
| issn | 2673-7647 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioinformatics |
| spelling | doaj-art-2c3ffc36ca9845b9a3b1e955fbbfc2a22025-08-20T02:36:00ZengFrontiers Media S.A.Frontiers in Bioinformatics2673-76472025-06-01510.3389/fbinf.2025.16028501602850Temporal GeneTerrain: advancing precision medicine through dynamic gene expression visualizationEhsan Saghapour0Ehsan Saghapour1Rahul Sharma2Delower Hossain3Delower Hossain4Kevin Song5Kevin Song6Zhandos Sembay7Zhandos Sembay8Jake Y. Chen9Jake Y. Chen10Department of Biomedical Informatics and Data Science, The University of Alabama at Birmingham, Birmingham, AL, United StatesSystems Pharmacology AI Research Center, The University of Alabama at Birmingham, Birmingham, AL, United StatesDepartment of Biomedical Informatics and Data Science, The University of Alabama at Birmingham, Birmingham, AL, United StatesSystems Pharmacology AI Research Center, The University of Alabama at Birmingham, Birmingham, AL, United StatesDepartment of Computer Science, The University of Alabama at Birmingham, Birmingham, AL, United StatesSystems Pharmacology AI Research Center, The University of Alabama at Birmingham, Birmingham, AL, United StatesDepartment of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, AL, United StatesDepartment of Biomedical Informatics and Data Science, The University of Alabama at Birmingham, Birmingham, AL, United StatesSystems Pharmacology AI Research Center, The University of Alabama at Birmingham, Birmingham, AL, United StatesDepartment of Biomedical Informatics and Data Science, The University of Alabama at Birmingham, Birmingham, AL, United StatesSystems Pharmacology AI Research Center, The University of Alabama at Birmingham, Birmingham, AL, United StatesIntroductionUnderstanding 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.MethodsTo 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.ResultsThe 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.DiscussionTemporal 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.https://www.frontiersin.org/articles/10.3389/fbinf.2025.1602850/fullcancer cell linesgene expressiondrug screeningprecision medicinebioinformaticsdata visualization |
| spellingShingle | Ehsan Saghapour Ehsan Saghapour Rahul Sharma Delower Hossain Delower Hossain Kevin Song Kevin Song Zhandos Sembay Zhandos Sembay Jake Y. Chen Jake Y. Chen Temporal GeneTerrain: advancing precision medicine through dynamic gene expression visualization Frontiers in Bioinformatics cancer cell lines gene expression drug screening precision medicine bioinformatics data visualization |
| title | Temporal GeneTerrain: advancing precision medicine through dynamic gene expression visualization |
| title_full | Temporal GeneTerrain: advancing precision medicine through dynamic gene expression visualization |
| title_fullStr | Temporal GeneTerrain: advancing precision medicine through dynamic gene expression visualization |
| title_full_unstemmed | Temporal GeneTerrain: advancing precision medicine through dynamic gene expression visualization |
| title_short | Temporal GeneTerrain: advancing precision medicine through dynamic gene expression visualization |
| title_sort | temporal geneterrain advancing precision medicine through dynamic gene expression visualization |
| topic | cancer cell lines gene expression drug screening precision medicine bioinformatics data visualization |
| url | https://www.frontiersin.org/articles/10.3389/fbinf.2025.1602850/full |
| work_keys_str_mv | AT ehsansaghapour temporalgeneterrainadvancingprecisionmedicinethroughdynamicgeneexpressionvisualization AT ehsansaghapour temporalgeneterrainadvancingprecisionmedicinethroughdynamicgeneexpressionvisualization AT rahulsharma temporalgeneterrainadvancingprecisionmedicinethroughdynamicgeneexpressionvisualization AT delowerhossain temporalgeneterrainadvancingprecisionmedicinethroughdynamicgeneexpressionvisualization AT delowerhossain temporalgeneterrainadvancingprecisionmedicinethroughdynamicgeneexpressionvisualization AT kevinsong temporalgeneterrainadvancingprecisionmedicinethroughdynamicgeneexpressionvisualization AT kevinsong temporalgeneterrainadvancingprecisionmedicinethroughdynamicgeneexpressionvisualization AT zhandossembay temporalgeneterrainadvancingprecisionmedicinethroughdynamicgeneexpressionvisualization AT zhandossembay temporalgeneterrainadvancingprecisionmedicinethroughdynamicgeneexpressionvisualization AT jakeychen temporalgeneterrainadvancingprecisionmedicinethroughdynamicgeneexpressionvisualization AT jakeychen temporalgeneterrainadvancingprecisionmedicinethroughdynamicgeneexpressionvisualization |