Brain-wide interpolation and conditioning of gene expression in the human brain using Implicit Neural Representations
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866913890812559360 |
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| author | Yu, Xizheng Torok, Justin Pandya, Sneha Pal, Sourav Singh, Vikas Raj, Ashish |
| author_facet | Yu, Xizheng Torok, Justin Pandya, Sneha Pal, Sourav Singh, Vikas Raj, Ashish |
| contents | In this paper, we study the efficacy and utility of recent advances in non-local, non-linear image interpolation and extrapolation algorithms, specifically, ideas based on Implicit Neural Representations (INR), as a tool for analysis of spatial transcriptomics data. We seek to utilize the microarray gene expression data sparsely sampled in the healthy human brain, and produce fully resolved spatial maps of any given gene across the whole brain at a voxel-level resolution. To do so, we first obtained the 100 top AD risk genes, whose baseline spatial transcriptional profiles were obtained from the Allen Human Brain Atlas (AHBA). We adapted Implicit Neural Representation models so that the pipeline can produce robust voxel-resolution quantitative maps of all genes. We present a variety of experiments using interpolations obtained from Abagen as a baseline/reference. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_11158 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Brain-wide interpolation and conditioning of gene expression in the human brain using Implicit Neural Representations Yu, Xizheng Torok, Justin Pandya, Sneha Pal, Sourav Singh, Vikas Raj, Ashish Genomics Machine Learning In this paper, we study the efficacy and utility of recent advances in non-local, non-linear image interpolation and extrapolation algorithms, specifically, ideas based on Implicit Neural Representations (INR), as a tool for analysis of spatial transcriptomics data. We seek to utilize the microarray gene expression data sparsely sampled in the healthy human brain, and produce fully resolved spatial maps of any given gene across the whole brain at a voxel-level resolution. To do so, we first obtained the 100 top AD risk genes, whose baseline spatial transcriptional profiles were obtained from the Allen Human Brain Atlas (AHBA). We adapted Implicit Neural Representation models so that the pipeline can produce robust voxel-resolution quantitative maps of all genes. We present a variety of experiments using interpolations obtained from Abagen as a baseline/reference. |
| title | Brain-wide interpolation and conditioning of gene expression in the human brain using Implicit Neural Representations |
| topic | Genomics Machine Learning |
| url | https://arxiv.org/abs/2506.11158 |