ArticleGenome medicine2025
MuCST: restoring and integrating heterogeneous morphology images and spatial transcriptomics data with contrastive learning.
Article in Genome medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Structural-information guided fusion for spatial domain identification from spatial transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- SPEAK: Spatial Prompting with Expert Aligned Knowledge for Tissue Domain Identification in Spatial Transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- spAttClu: a spatial domain clustering model leveraging spatially weighted graph attention and contrastive learning.Bioinformatics (Oxford, England) · 2026Article
- Network model for alignment, stitching and slice-to-volume 3D reconstruction of large-scale spatially resolved slices.Nature communications · 2026Article
- Signal-based spatial domain identification of spatially resolved transcriptomics with multigraph fusion.Briefings in bioinformatics · 2026Article
- Network models for bridging denoising and identifying spatial domains of spatially resolved transcriptomics.PLoS computational biology · 2026Article
- Unveiling spatial domains from spatial multi-omics data using dual-graph regularized ensemble learning.Communications biology · 2025Article
- MuCST: restoring and integrating heterogeneous morphology images and spatial transcriptomics data with contrastive learning.Genome medicine · 2025Article
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3 authors.
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Abstract
Spatially resolved transcriptomics (SRT) simultaneously measure spatial location, histology images, and transcriptional profiles of cells or regions in undissociated tissues. Integrative analysis of multi-modal SRT data holds immense potential for understanding biological mechanisms. Here, we present a flexible multi-modal contrastive learning for the integration of SRT data (MuCST), which joins denoising, heterogeneity elimination, and compatible feature learning. MuCST accurately identifies spatial domains and is applicable to diverse datasets platforms. Overall, MuCST provides an alternative for integrative analysis of multi-modal SRT data ( https://github.com/xkmaxidian/MuCST ).
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