TransST: Transfer Learning Embedded Spatial Factor Modeling of Spatial Transcriptomics Data
This work addresses the problem of enhancing spatial transcriptomics analysis for biomedical researchers, offering an incremental improvement by integrating transfer learning to boost accuracy in cell clustering.
The authors tackled the challenge of extracting reliable biological signals from low-resolution spatial transcriptomics data by proposing TransST, a transfer learning framework that leverages external cell-labeled information to infer cell-level heterogeneity, resulting in improved identification of biologically meaningful cell clusters, such as distinguishing cancer subgroups and separating adipose from connective tissues in a breast cancer study.
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.