Abstract Number:
3616
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Jiazhang Cai (1), Huimin Cheng (2), Wenxuan Zhong (3), Guocheng Yuan (4), Ping Ma (3)
Institutions:
(1) N/A, N/A, (2) Boston University, MA, (3) University of Georgia, N/A, (4) Icahn School of Medicine at Mount Sinai, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
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|
Sponsors:
Section on Statistics in Genomics and Genetics
Tracks:
Miscellaneous
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