Abstract
Spatial transcriptomics is a groundbreaking technology, enabling simultaneous profiling of gene expression and spatial orientation within biological tissues. Yet when analyzing spatial transcriptomics data, effective integration of expression and spatial information poses considerable analytical challenges. Although many methods have been developed to address this issue, many are platform specific and lack the general applicability to analyze diverse datasets. In this article, we propose a method called the weighted ensemble method for spatial transcriptomics (WEST) that utilizes ensemble techniques to improve the performance and robustness of spatial transcriptomics data analytics. We compare the performance of WEST with six methods on both synthetic and real-world datasets. WEST represents a significant advance in detecting spatial domains, offering improved accuracy and flexibility compared to existing methods, making it a valuable tool for spatial transcriptomics data analytics.