1160-DeepSpaCE: for Super-Resolution Spatial Transcriptomics
This paper introduces DeepSpaCE, a deep-learning tool designed to enhance spatial transcriptomics by predicting gene-expression profiles from standard histological images. Traditional spatial gene mapping is often hindered by high experimental costs and technical errors, such as permeabilization issues that leave gaps in tissue data. By utilizing convolutional neural networks, this method enables super-resolution imaging of gene activity and the imputation of missing data in consecutive tissue sections without additional experiments. Researchers validated the model using human breast cancer…
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