CASPER: Cross-modal Alignment of Spatial and single-cell Profiles for Expression Recovery
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912718565408768 |
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| author | Kumar, Amit Kaur, Maninder Mall, Raghvendra Gupta, Sukrit |
| author_facet | Kumar, Amit Kaur, Maninder Mall, Raghvendra Gupta, Sukrit |
| contents | Spatial Transcriptomics enables mapping of gene expression within its native tissue context, but current platforms measure only a limited set of genes due to experimental constraints and excessive costs. To overcome this, computational models integrate Single-Cell RNA Sequencing data with Spatial Transcriptomics to predict unmeasured genes. We propose CASPER, a cross-attention based framework that predicts unmeasured gene expression in Spatial Transcriptomics by leveraging centroid-level representations from Single-Cell RNA Sequencing. We performed rigorous testing over four state-of-the-art Spatial Transcriptomics/Single-Cell RNA Sequencing dataset pairs across four existing baseline models. CASPER shows significant improvement in nine out of the twelve metrics for our experiments. This work paves the way for further work in Spatial Transcriptomics to Single-Cell RNA Sequencing modality translation. The code for CASPER is available at https://github.com/AI4Med-Lab/CASPER. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_15139 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CASPER: Cross-modal Alignment of Spatial and single-cell Profiles for Expression Recovery Kumar, Amit Kaur, Maninder Mall, Raghvendra Gupta, Sukrit Genomics Artificial Intelligence Machine Learning Spatial Transcriptomics enables mapping of gene expression within its native tissue context, but current platforms measure only a limited set of genes due to experimental constraints and excessive costs. To overcome this, computational models integrate Single-Cell RNA Sequencing data with Spatial Transcriptomics to predict unmeasured genes. We propose CASPER, a cross-attention based framework that predicts unmeasured gene expression in Spatial Transcriptomics by leveraging centroid-level representations from Single-Cell RNA Sequencing. We performed rigorous testing over four state-of-the-art Spatial Transcriptomics/Single-Cell RNA Sequencing dataset pairs across four existing baseline models. CASPER shows significant improvement in nine out of the twelve metrics for our experiments. This work paves the way for further work in Spatial Transcriptomics to Single-Cell RNA Sequencing modality translation. The code for CASPER is available at https://github.com/AI4Med-Lab/CASPER. |
| title | CASPER: Cross-modal Alignment of Spatial and single-cell Profiles for Expression Recovery |
| topic | Genomics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2511.15139 |