Riemannian Metric Learning for Alignment of Spatial Multiomics

Abstract excerpt

Recent spatial technologies measure the transcriptome, epigenome, proteome, metabolome and other modalities from thousands of cells across a tissue. Most assays typically profile only one modality from a tissue slice, raising the question of how to align spatial data from heterogeneous feature spaces. While multiple approaches have been developed for multi-modal integration of single-cell datasets, few existing te...

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• AI in cancer detection
• Cancer Cells and Metastasis
• Cell Image Analysis Techniques
• Ferroptosis and cancer prognosis
• Gene expression and cancer classification
• Neonatal Respiratory Health Research
• Radiomics and Machine Learning in Medical Imaging
• Single-cell and spatial transcriptomics

Identifiers and source

Literature Corpus work

f33ad381-628a-5f4a-85ce-c2f8118023b5

DOI

10.64898/2025.12.09.693237