Spatially Informed and Direction Sensitive Representation Learning for Spatial Transcriptomics
[q-bio.QM] Autorzy: Azim Dehghani Amirabad, Ali Khodabandeh Yalabadi, Artem Moskalev i 4 więcej
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…
- Kategoria arxiv
- q-bio.QM
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- q-bio.QM