69 WEST: An Ensemble Method for Spatial Transcriptomics Analysis

Huimin Cheng Co-Author
Boston University
 
Wenxuan Zhong Co-Author
University of Georgia
 
Guocheng Yuan Co-Author
Icahn School of Medicine at Mount Sinai
 
Ping Ma Co-Author
University of Georgia
 
Jiazhang Cai First Author
 
Jiazhang Cai Presenting Author
 
Tuesday, Aug 6: 10:30 AM - 12:20 PM
3616 
Contributed Posters 
Oregon Convention Center 
Spatial transcriptomics emerges as 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 novel method called 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 five popular 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.

Keywords

spatial transcriptomics,

Visium

seqFISH

nsemble learning

deep learning 

Abstracts


Main Sponsor

Section on Statistics in Genomics and Genetics