An open problem in spatial transcriptomics is inferring single-cell information. New computational methods are needed despite recent advances in technology. Mathematical tools now fill this gap without requiring prior knowledge of cell boundaries.
Existing spatial transcriptomics methods face trade-offs between spatial resolution, transcriptome depth, and sample size. Current tools focus on predicting cell types from multicellular and subcellular data, mainly integrating with single-cell or single-nucleus RNA sequencing data.
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