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AESTETIK: Convolutional autoencoder for learning spot representations from spatial transcriptomics and morphology data
DeepSpot: Deep learning model for predicting spatial transcriptomics from H&E histopathology images. Supports spot-level (Visium) and single-cell (Xenium) resolution.
DeepSpot2Cell: Predicting virtual single-cell spatial transcriptomics from H&E images using spot-level supervision
Multimodal foundation model predicting transcriptome-wide virtual spatial transcriptomics from histology.
Repository accompanying the WiNN paper, containing reproducible analysis notebooks for the simulation benchmark and public multi-batch dataset, together with code to reproduce benchmarking results, supplementary analyses, and figure/table generation for the manuscript.
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