Abstract Number:
3764
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Shushan Wu (1), Jiazhang Cai (2), Huimin Cheng (3), Wenxuan Zhong (4), Guo-Cheng Yuan (5), Ping Ma (4)
Institutions:
(1) University Of Georgia, N/A, (2) N/A, N/A, (3) Boston University, MA, (4) University of Georgia, N/A, (5) Dana-Farber Cancer Institute, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
Spatial transcriptomics has gained significant interest since 2020 due to its ability to provide spatial data with gene expression information. According to the spatial information, more hidden tissue structures and biological functions are revealed. Numerous studies have focused on detecting spatial domains by effectively combining spatial and gene expression data. However, due to the intricate nature of spatial domains, many existing methods fall short, often limited by their focus on smaller neighboring areas. In this paper, we introduce the Spatial T-SNE, which also takes the cell type proportion of the spatial domain into account. Our method uniquely differentiates between spatial domains based on varying cell type proportions, employing an iterative updating algorithm. We test the performance of Spatial T-SNE with several popular spatial domain detecting methods on three published datasets. The results demonstrate that Spatial T-SNE more accurately reflects annotated spatial patterns, highlighting its effectiveness in spatial transcriptomic analysis.
Keywords:
Spatial Transcriptomics|Spatial Domain Detection|T-SNE| | |
Sponsors:
Biometrics Section
Tracks:
Genomics, Metabolomics, Microbiome and NextGen Sequencing
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