MuCST: restoring and integrating heterogeneous morphology images and spatial transcriptomics data with contrastive learning
Yu Wang,
Xiaoke Ma
Abstract:Spatially resolved transcriptomics simultaneously measure the spatial location, histology images, and transcriptional profiles of the same cells or regions in undissociated tissues. Integrative analysis of multi-modal spatially resolved data holds immense potential for understanding the mechanisms of biology. Here we present a flexible multi-modal contrastive learning for the integration of spatially resolved transcriptomics (MuCST), which jointly perform denoising, elimination of heterogeneity, and compatible… Show more
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