Spatially Informed and Direction Sensitive Representation Learning for Spatial Transcriptomics

arXiv:2610.28579v1 Announce Type: cross 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 pro

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