TransST: transfer learning embedded spatial factor modeling of spatial transcriptomics data.

ABSTRACT:

Background

Spatial transcriptomics have emerged as a powerful tool in biomedical research because of its ability to capture both the spatial contexts and abundance of the complete RNA transcript profile in organs of interest. However, limitations of the technology such as the relatively low resolution and comparatively insufficient sequencing depth make it difficult to reliably extract real biological signals from these data. To alleviate this challenge, we propose a novel transfer learning framework, referred to as TransST, to adaptively leverage the cell-labeled information from external sources in inferring cell-level heterogeneity of a target spatial transcriptomics data.

Results

Applications in several real studies as well as a number of simulation settings show that ou

SUBMITTER: Liu SS 

PROVIDER: S-EPMC12593783 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature