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Abstract Depicting gene expression in a spatial context through spatial transcriptomics is beneficial for inferring cellular mechanisms. Identifying spatially variable genes is a crucial step in leveraging spatial transcriptome data to understand intricate spatial dynamics. In this study, we developed Spanve, a nonparametric statistical method for detecting spatially variable genes in large-scale spatial transcriptomics datasets by quantifying expression differences between each spot or cell and its local neighbors. This method offers a nonparametric approach for identifying spatial dependencies in gene expression without distributional assumptions. Compared with existing methods, Spanve yields fewer false positives, leading to more accurate identification of spatially variable genes. Furthermore, Spanve improves the performance of downstream spatial transcriptomics analyses including spatial domain detection and cell type deconvolution. These results show the broad application potential of Spanve in advancing our understanding of spatial gene expression patterns within complex tissue microenvironments. Spanve is publicly available at https://github.com/zjupgx/Spanve and https://ngdc.cncb.ac.cn/biocode/tool/BT7724.
Chen et al. (Mon,) studied this question.