PEaRL: Pathway-Enhanced Representation Learning for Gene and Pathway Expression Prediction from Histology
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| author | Majumder, Sejuti Kapse, Saarthak Bhattacharya, Moinak Xu, Xuan Yurovsky, Alisa Prasanna, Prateek |
| author_facet | Majumder, Sejuti Kapse, Saarthak Bhattacharya, Moinak Xu, Xuan Yurovsky, Alisa Prasanna, Prateek |
| contents | Integrating histopathology with spatial transcriptomics (ST) provides a powerful opportunity to link tissue morphology with molecular function. Yet most existing multimodal approaches rely on a small set of highly variable genes, which limits predictive scope and overlooks the coordinated biological programs that shape tissue phenotypes. We present PEaRL (Pathway Enhanced Representation Learning), a multimodal framework that represents transcriptomics through pathway activation scores computed with ssGSEA. By encoding biologically coherent pathway signals with a transformer and aligning them with histology features via contrastive learning, PEaRL reduces dimensionality, improves interpretability, and strengthens cross-modal correspondence. Across three cancer ST datasets (breast, skin, and lymph node), PEaRL consistently outperforms SOTA methods, yielding higher accuracy for both gene- and pathway-level expression prediction (up to 58.9 percent and 20.4 percent increase in Pearson correlation coefficient compared to SOTA). These results demonstrate that grounding transcriptomic representation in pathways produces more biologically faithful and interpretable multimodal models, advancing computational pathology beyond gene-level embeddings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_03455 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PEaRL: Pathway-Enhanced Representation Learning for Gene and Pathway Expression Prediction from Histology Majumder, Sejuti Kapse, Saarthak Bhattacharya, Moinak Xu, Xuan Yurovsky, Alisa Prasanna, Prateek Computer Vision and Pattern Recognition Integrating histopathology with spatial transcriptomics (ST) provides a powerful opportunity to link tissue morphology with molecular function. Yet most existing multimodal approaches rely on a small set of highly variable genes, which limits predictive scope and overlooks the coordinated biological programs that shape tissue phenotypes. We present PEaRL (Pathway Enhanced Representation Learning), a multimodal framework that represents transcriptomics through pathway activation scores computed with ssGSEA. By encoding biologically coherent pathway signals with a transformer and aligning them with histology features via contrastive learning, PEaRL reduces dimensionality, improves interpretability, and strengthens cross-modal correspondence. Across three cancer ST datasets (breast, skin, and lymph node), PEaRL consistently outperforms SOTA methods, yielding higher accuracy for both gene- and pathway-level expression prediction (up to 58.9 percent and 20.4 percent increase in Pearson correlation coefficient compared to SOTA). These results demonstrate that grounding transcriptomic representation in pathways produces more biologically faithful and interpretable multimodal models, advancing computational pathology beyond gene-level embeddings. |
| title | PEaRL: Pathway-Enhanced Representation Learning for Gene and Pathway Expression Prediction from Histology |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.03455 |