Reconstructing multi-scale tissue spatial architecture from single-cell RNA-seq with REMAP

Abstract excerpt

Understanding spatial organization of cells is critical for deciphering tissue function and disease. Single-cell RNA-sequencing (scRNA-seq) profiles transcriptomes at scale but loses spatial context, while spatial transcriptomics (ST) preserves spatial information but is constrained by cost and gene coverage. Here, we present REMAP, a deep learning framework that integrates gene expression with neighborhood-level...

Topics

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• Algorithms
• Cancer Cells and Metastasis
• Cancer Genomics and Diagnostics
• Cluster Analysis
• Neural Networks, Computer
• Phenotype
• Radiomics and Machine Learning in Medical Imaging
• Single-cell and spatial transcriptomics

Identifiers and source

Literature Corpus work

1c3dd90c-1ee7-52f2-934e-ed12429d85e3

DOI

10.64898/2026.02.21.707167