Azim Dehghani Amirabad, Ali Khodabandeh Yalabadi, Artem Moskalev, Yun-Ching Chen, Mehdi Pirooznia, Scott Oloff, Rui Liao · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28579 · open-access PDF
细胞状态是由内在的基因表达程序和细胞居住的空间微环境形成的。空间转码学使这种背景可测量,但有效的代表性学习需要捕捉空间信息的分子信号和细胞间的组织。现有方法仅部分解决这些挑战:传统的基因选择可以忽视空间结构的信号,而编码细胞关系的方法往往要么将细胞的微环境减少到靠近,抛弃方向,要么依赖于ABS。
Machine translation (Meta M2M100); the English original follows.
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 abs
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