Molecular assays such as spatial transcriptomics facilitate fine-grained identification of cell types for analyzing tumor microenvironments. However, they are expensive to acquire in routine clinical practice compared to hematoxylin and eosin (H&E) images. With molecular guidance, such images can distinguish cell linea...
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
G2T (Gene-to-Tissue), a generative deep learning model that reassembles a tissue from gene expression by predicting the matrix of pairwise distances between cells in a learned embedding space, yields a compact, scalable building block for reconstructing tissue from dissociated cells.
Sebastian Birk, Fabian J. Theis, M. Lotfollahi· bioRxiv· 0 citations
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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