spOT-NMF: Optimal Transport-Based Matrix Factorization for Accurate Deconvolution of Spatial Transcriptomics

Abstract excerpt

Spatial transcriptomics technologies advance our understanding of complex biology by directly profiling cellular organization within tissues. However, accurate deconvolution of cell types and functional states remains challenging as most current computational methods either rely on high-quality matched single-cell reference profiles (often lacking for many tissues or disease states), struggle across spatial resolu...

Topics

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• Biomarkers, Tumor
• Ferroptosis and cancer prognosis
• Gene Expression Profiling
• Machine Learning
• Neoplasms
• Phenotype
• Single-cell and spatial transcriptomics
• Transcriptome

Identifiers and source

Literature Corpus work

27502d7f-5b33-5cc6-968b-6a4299ec592b

DOI

10.1101/2025.08.02.668292