SNMF: Ultrafast, Spatially-Aware Deconvolution for Spatial Transcriptomics

Abstract excerpt

Sequencing-based spatial transcriptomics has revolutionized the study of tissue architecture, but its ‘spots’ often contain multiple cells, creating a key computational challenge, termed deconvolution, to decipher each spot’s cell-type composition. Reference-free deconvolution methods avoid the need for a matched single-cell RNA-seq dataset, but typically neglect the spatial correlation between neighboring spots a...

Topics

Open a Topic to create a Post that cites this publication.

• Biomarkers, Tumor
• Gene Expression Profiling
• Machine Learning
• Neoplasms
• Phenotype
• Transcriptome

Identifiers and source

Literature Corpus work

e1f454a7-4372-5997-9e35-3e7363f91b53

DOI

10.64898/2026.03.17.712043