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G-LATO: Inference of Spatial Latent Ordering via Deep Gaussian Processes

Zago, M.; Mukherjee, S.; Schleicher, J. T.; Bürkner, P.; Tabatabai, G.; Claassen, M.

2026-06-29 bioinformatics
10.64898/2026.06.23.734031 bioRxiv
Show abstract

Spatial transcriptomics enables the study of cells within their native tissue context, yet identifying gradients of cellular development remains challenging. We introduce a deep Gaussian process model to address this gap. Our method recovers spatially smooth gradients explaining observed gene expression. We illustrate our method on healthy liver and glioblastoma data in reconstructing known spatial organisation and uncovering new pathological gradients, thus providing robust inference for spatial biology.

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