Enhancing Spatial Transcriptomics via Spatially Constrained Matrix Decomposition with EDGES.

ABSTRACT: Spatial transcriptomics (ST) technologies revolutionize biomedical research by providing unprecedented insights into tissue architecture and disease mechanisms. While imaging-based ST technologies achieve single-cell spatial resolution, they face inherent limitations in gene detection capacity and measurement accuracy of expression profiles. Although computational approaches make notable progress, current methods remain challenged by insufficient integration of spatial context and systematic biases toward the single-cell RNA sequencing distribution. To address these limitations, EDGES is developed a spatially constrained non-negative matrix factorization framework that simultaneously predicts undetected gene expression and denoises measured transcriptional profiles. EDGES incorporates spat

SUBMITTER: Zhao J 

PROVIDER: S-EPMC12622516 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature