Spatial transcriptomics technologies offer exciting opportunities for multi-modal investigations of cells and tissue structures. However, spatial transcriptomics samples are highly heterogeneous, making it challenging to make robust comparisons between samples from different individuals or conditions. This project will apply statistical and AI techniques to develop methods and software to enable robust comparisons across spatial transcriptomics samples.
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