STMiner: Gene-centric spatial transcriptomics for deciphering tumor tissues.

ABSTRACT: Analyzing spatial transcriptomics data from tumor tissues poses several challenges beyond those of healthy samples, including unclear boundaries between different regions, uneven cell densities, and relatively higher cellular heterogeneity. Collectively, these bias the background against which spatially variable genes are identified, which can result in misidentification of spatial structures and hinder potential insight into complex pathologies. To overcome this problem, STMiner leverages 2D Gaussian mixture models and optimal transport theory to directly characterize the spatial distribution of genes rather than the capture locations of the cells expressing them (spots). By effectively mitigating the impacts of both background bias and data sparsity, STMiner reveals key gene sets and spa

SUBMITTER: Sun P 

PROVIDER: S-EPMC11872602 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature