Spanve: an Statistical Method to Detect Clustering-friendly Spatially Variable Genes in Large-scale Spatial Transcriptomics Data

The depiction of in situ gene expression through spatial transcriptomics facilitates the inference of cell function mechanisms. To build spatial maps of transcriptomes, the first and crucial step is to identify spatially variable (SV) genes. However, current methods fall short in dealing with large-scale spatial transcriptomics data and may result in a high false positive rate due to the modeling of gene expression into parametric distributions. This paper introduces Spanve (https://github.com/zjupgx/Spanve), a non-parametric statistical approach based on modeling space dependence as a distance of two distributions for detecting SV genes. The high computing efficiency and accuracy of Spanve is demonstrated through comprehensive benchmarking. Additionally, Spanve can detect clustering-friendly SV genes and spatially variable co-expression, facilitating the identification of spatial tissue domains by an imputation.