HEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics.

Jul 08, 2024

Experts: Xin Yuan,Yanran Ma,Ruitian Gao,Shuya Cui,Yifan Wang,Botao Fa,Shiyang Ma,Ting Wei,Shuangge Ma,Zhangsheng Yu

Identifying spatially variable genes (SVGs) is crucial for understanding the spatiotemporal characteristics of diseases and tissue structures, posing a distinctive challenge in spatial transcriptomics research. We propose HEARTSVG, a distribution-free, test-based method for fast and accurately identifying spatially variable genes in large-scale spatial transcriptomic data. Extensive simulations demonstrate that HEARTSVG outperforms state-of-the-art methods with higher scores (average Score=0.948), improved computational efficiency, scalability, and reduced false positives (FPs). Through analysis of twelve real datasets from various spatial transcriptomic technologies, HEARTSVG identifies a greater number of biologically significant SVGs (average AUC = 0.792) than other comparative methods without prespecifying spatial patterns. Furthermore, by clustering SVGs, we uncover two distinct tumor spatial domains characterized by unique spatial expression patterns, spatial-temporal locations, and biological functions in human colorectal cancer data, unraveling the complexity of tumors.
© 2024. The Author(s).

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ABOUT THE EXPERTS

Xin Yuan,Yanran Ma,Ruitian Gao,Shuya Cui,Yifan Wang,Botao Fa,Shiyang Ma,Ting Wei,Shuangge Ma,Zhangsheng Yu

Xin Yuan

Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.

SJTU-Yale Joint Center for Biostatistics and Data Science Organization, Shanghai Jiao Tong University, Shanghai, China.

Yanran Ma

Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.

Ruitian Gao

Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.

Shuya Cui

Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.

SJTU-Yale Joint Center for Biostatistics and Data Science Organization, Shanghai Jiao Tong University, Shanghai, China.

Yifan Wang

Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.

Botao Fa

Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Xi’an Jiaotong University, Xi’an, Shanxi, China.

Shiyang Ma

Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Ting Wei

Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.

Shuangge Ma

SJTU-Yale Joint Center for Biostatistics and Data Science Organization, Shanghai Jiao Tong University, Shanghai, China. shuangge.ma@yale.edu.

Department of Biostatistics, Yale University, New Haven, USA. shuangge.ma@yale.edu.

Zhangsheng Yu

Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China. yuzhangsheng@sjtu.edu.cn.

SJTU-Yale Joint Center for Biostatistics and Data Science Organization, Shanghai Jiao Tong University, Shanghai, China. yuzhangsheng@sjtu.edu.cn.

Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China. yuzhangsheng@sjtu.edu.cn.

Center for Biomedical Data Science, Translational Science Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China. yuzhangsheng@sjtu.edu.cn.