arXiv cs.AI· Azim Dehghani Amirabad, Ali Khodabandeh Yalabadi, Artem Moskalev, Yun-Ching Chen, Mehdi Pirooznia, Scott Oloff, Rui Liao·· 12 小时前AI 评分22
Tessera：面向空间转录组的方向敏感空间表示学习框架
Spatially Informed and Direction Sensitive Representation Learning for Spatial Transcriptomics
AI 导读
Tessera 是一个空间表示学习框架，结合空间信息驱动的基因选择与几何归纳偏置，在建模细胞间相对关系时保持平移不变性同时保留方向信息。在留出组织和不同技术平台上，Tessera 的表示质量优于现有空间基础模型，并可零样本泛化到比训练时大 16 倍的空间上下文。该工作已被 NeurIPS 2026 AI4DD Workshop 接收。
正文 · 原文
Abstract:Cellular states are shaped by both intrinsic gene expression programs and the spatial microenvironment in which cells reside. Spatial transcriptomics makes this context measurable, but effective representation learning requires capturing both spatially informative molecular signals and intercellular organization. Existing approaches address these challenges only partially: conventional gene selection can overlook spatially structured signals, while methods for encoding cell-cell relationships often either reduce a cell's microenvironment to proximity alone, discarding direction, or rely on absolute coordinates tied to an arbitrary tissue frame. Consequently, cells with similar neighbor distances but distinct spatial organization may receive overly similar representations, despite experiencing different biological contexts that shape their cellular states. We introduce Tessera, a spatial representation learning framework that addresses both molecular selection and cellular context. Tessera combines spatially informed gene selection with a geometric inductive bias that contextualizes each cell within its local tissue environment. By modeling relative cell-cell relationships, Tessera is translation-invariant while preserving direction, allowing neighboring cells to contribute differently according to their spatial context and cellular state. Across held-out tissues and technologies, Tessera outperforms existing spatial foundation models in representation quality and generalizes zero-shot to contexts up to $16\times$ larger than those seen during training.
| Comments: | This manuscript has been accepted at the NeurIPS 2026 Workshop on AI for Drug Discovery: Bridging the Translation Gap (AI4DD) |
| Subjects: | Quantitative Methods (q-bio.QM); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.28579 [q-bio.QM] |
| (or arXiv:2610.28579v1 [q-bio.QM] for this version) | |
| https://doi.org/10.48550/arXiv.2610.28579 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ali Khodabandeh Yalabadi Mr. [view email]
[v1]
Tue, 20 Oct 2026 17:32:57 UTC (3,132 KB)
来源：arXiv cs.AI · arxiv.org