Spatially Informed and Direction Sensitive Representation Learning for Spatial Transcriptomics
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…
We haven't written up this one. arXiv cs.AI has the full story — the link below goes straight to it.
