Journal Article

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

Wang, Shikun, Yuan, Ming, Chen, Yuxuan, Rustgi, Anil K., Liu, Shuo Shuo, Hu, Jianhua

2025

Overview

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 our approach significantly improves existing techniques. For example, in the breast cancer study, TransST successfully identifies five biologically meaningful cell clusters, including the two subgroups of cancer in situ and invasive cancer; in addition, only TransST is able to separate the adipose tissues from the connective issues among all the studied methods. Conclusions In summary, the proposed method TransST is both effective and robust in identifying cell subclusters and detecting corresponding driving biomarkers in spatial transcriptomics data.

• Publisher: BioMed Central,BioMed Central Ltd,BMC
• Subject: Algorithms / Analysis / Bioinformatics / Biological markers / Biomedical and Life Sciences / Breast Neoplasms - genetics / Clustering / Computational Biology - methods / Computational Biology/Bioinformatics / Computer Appl. in Life Sciences / Factor model / Gene Expression Profiling - methods / Humans / Life Sciences / Machine Learning / Markov random field / Methods / Microarrays / RNA sequencing / Simulation methods / Spatial analysis (Statistics) / Spatial transcriptomics / Transcriptome / Transfer learning