Metraipolis › archive › dc_994fa31462ca

From Cross-Section to Cross-Omics: Conditional Cell Nucleus Image Editing in Spatial Transcriptomics — arXiv:2611.00226

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.00226
License (as archived): CC0-1.0
Archived: 2026-11-03T10:21:39.910Z
SHA-256 of the archived text: d60b7f0ea22f434d88ec0bb53c313a7a350ea31010ab3274e36915e2284bf7ab
Stable URL of this page: https://metraipolis.com/documento/dc_994fa31462ca

How to cite

From Cross-Section to Cross-Omics: Conditional Cell Nucleus Image Editing in Spatial Transcriptomics — arXiv:2611.00226 — arXiv (en), https://arxiv.org/abs/2611.00226 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_994fa31462ca (Metraipolis archive dc_994fa31462ca, sha256 d60b7f0ea22f434d88ec0bb53c313a7a350ea31010ab3274e36915e2284bf7ab)

BibTeX

@misc{dc994fa31462ca,
  title = {From Cross-Section to Cross-Omics: Conditional Cell Nucleus Image Editing in Spatial Transcriptomics — arXiv:2611.00226},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.00226},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_994fa31462ca; sha256 d60b7f0ea22f434d88ec0bb53c313a7a350ea31010ab3274e36915e2284bf7ab},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.00226. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Spatial transcriptomics (ST) platforms simultaneously measure gene expression and capture cell nucleus images at single-cell resolution. A fundamental property of tissue preparation introduces a confounding relationship between the two measurement modalities: sectioning a three-dimensional tissue into a thin slice produces a two-dimensional cross-section whose area depends on the cutting angle. Larger cross-sections expose more cytoplasm, yielding higher mRNA counts, while pathomics features such as nuclear area are measured directly from the same 2D boundary. This sectioning geometry creates an apparent correlation between gene expression and pathomics that reflects the measurement process rather than independent biology. Existing gene-to-image methods generate cell images from gene features but do not support condition-driven editing of existing images across both omics layers. We apply the Manifold-Aware Diffusion (MAD) framework to this dual-omics editing setting, enabling targeted modification of one feature group while preserving the other. Evaluated on podocyte cells from CosMx SMI kidney tissue, MAD achieves MAE=0.6636 and FID=25.19 for pathomics editing, outperforming all compared baselines. A controlled simulation study further confirms that the VAE component in MAD better preserves cross-group independence than CCDM (MAD without VAE).

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.00226 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T10:19:49.742Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).