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
Transcriptomics spasial membuat konteks ini dapat diukur, tetapi pembelajaran representasi yang efektif membutuhkan menangkap sinyal molekuler yang informatif secara spasial dan organisasi antar sel. Pendekatan yang ada hanya membahas tantangan-tantangan ini sebagian: seleksi gen konvensional dapat mengabaikan sinyal yang terstruktur secara spasial, sedangkan metode untuk mengkodekan hubungan sel-sel seringkali mengurangi lingkungan mikro sel ke kedekatan saja, membuang arah, atau bergantung pada 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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