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| Format: | Preprint |
| Published: |
2025
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| Online Access: | https://arxiv.org/abs/2511.18238 |
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| _version_ | 1866915665750786048 |
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| author | Samadzelkava, Aya |
| author_facet | Samadzelkava, Aya |
| contents | Alzheimer's disease (AD) emerges from a complex interplay of molecular, cellular, and network-level disturbances that are not easily captured by traditional reductionist frameworks. Conventional analyses of gene expression often rely on thresholded correlation networks or clustering-based module detection, approaches that may obscure nonlinear structure and higher-order organization. Here, we introduce a comparative topological framework that makes use of topological data analysis (TDA) and the Mapper algorithm to detect discontinuities - localized disruptions in the topology of gene co-expression space between healthy and AD brain tissue. Using gene expression data from 3 brain regions, we mapped how AD reshapes the global topology of gene-gene relationships. Discontinuity hotspots were identified via variability-based node scoring and subjected to Gene Ontology Biological Process enrichment analysis. This work illustrates the potential of TDA to uncover disease-relevant structure in high-dimensional transcriptomic data and motivates broader application of shape-based comparative methods in neurodegeneration research and other areas that benefit from comparative analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_18238 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Detecting Discontinuities in the Topology of Alzheimers gene Co-expression Samadzelkava, Aya Quantitative Methods Alzheimer's disease (AD) emerges from a complex interplay of molecular, cellular, and network-level disturbances that are not easily captured by traditional reductionist frameworks. Conventional analyses of gene expression often rely on thresholded correlation networks or clustering-based module detection, approaches that may obscure nonlinear structure and higher-order organization. Here, we introduce a comparative topological framework that makes use of topological data analysis (TDA) and the Mapper algorithm to detect discontinuities - localized disruptions in the topology of gene co-expression space between healthy and AD brain tissue. Using gene expression data from 3 brain regions, we mapped how AD reshapes the global topology of gene-gene relationships. Discontinuity hotspots were identified via variability-based node scoring and subjected to Gene Ontology Biological Process enrichment analysis. This work illustrates the potential of TDA to uncover disease-relevant structure in high-dimensional transcriptomic data and motivates broader application of shape-based comparative methods in neurodegeneration research and other areas that benefit from comparative analysis. |
| title | Detecting Discontinuities in the Topology of Alzheimers gene Co-expression |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2511.18238 |