Tessera Preprint Offers Direction-Aware Spatial Transcriptomics Representation Learning
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An arXiv preprint introduces Tessera, a spatial representation learning framework for spatial transcriptomics that tackles two gaps: gene selection and modeling of cell-cell relationships. Tessera combines spatially informed gene selection with a geometric inductive bias that preserves direction, using relative cell-cell relationships rather than absolute coordinates, which yields translation invariance while still distinguishing direction. On held-out tissues and technologies, the framework outperforms existing spatial foundation models in representation quality and generalizes zero-shot to tissue contexts up to 16 times larger than those seen during training. The work is currently a preprint and has not yet passed peer review, so its validation by the broader spatial transcriptomics community is still open.
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- arxiv.orgSpatially Informed and Direction Sensitive Representation Learning for Spatial Transcriptomics
An arXiv preprint proposes Tessera, a spatial representation learning framework for spatial transcriptomics that addresses shortcomings in gene selection and modeling of cell-cell relationships. It combines spatially informed gene selection with a direction-preserving geometric inductive bias, using relative cell-cell relationships instead of absolute coordinates to achieve translation invariance while distinguishing direction. On held-out tissues and technologies, Tessera outperforms existing spatial foundation models in representation quality and generalizes zero-shot to contexts up to 16 times larger than those seen during training.
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