TransST: transfer learning embedded spatial factor modeling of spatial transcriptomics data
Shuo Shuo Liu, Shikun Wang, Yuxuan Chen, Anil K. Rustgi, Ming Yuan, Jianhua Hu
TL;DR
TransST is a new method that improves the analysis of spatial transcriptomics data by using transfer learning to better identify cell clusters and biomarkers.
Contribution
TransST introduces a novel transfer learning framework to enhance the analysis of spatial transcriptomics data by leveraging external cell-labeled information.
Findings
TransST successfully identifies five biologically meaningful cell clusters in a breast cancer study.
TransST uniquely separates adipose tissues from connective tissues in spatial transcriptomics data.
The method is shown to be effective and robust in identifying cell subclusters and biomarkers.
Abstract
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. 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…
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Taxonomy
TopicsGene expression and cancer classification