CellBridge is introduced, a multi-modal contrastive learning framework pretrained on 16M cells with paired H&E and spatial profiles, for transferring molecular information into morphological representations that consistently achieves smaller prediction sets, while reducing class-wise coverage gaps.
Spatial transcriptomics maps gene expression at cellular resolution, revealing how cells organize into multicellular niches. Yet computational analyses remain dataset-specific, without a transferable representation of tissue organization that generalizes across datasets, tasks and tissues or predicts how tissues behave...
Sebastian Birk, M. V. Sanian, Amirhossein Vahidi et al.· bioRxiv· 4 citations· ⚡1
SQUINT is presented, a graph vector-quantized variational autoencoder that learns two disjoint codebooks per cell from a shared architecture that outperforms or is competitive with strong baselines on identification and achieves the most faithful cross-section integration.
Sebastian Birk, Arpit Merchant, Amirhossein Vahidi et al.· bioRxiv· 0 citations
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