Adding highly variable genes to spatially variable genes can improve cell type clustering performance in spatial transcriptomics data

Abstract

The workflow for this study is shown in [vbaf285-F1]. It starts with spatial transcriptomics data, which has two components: a gene expression matrix and spatial data, which consists of the spatial coordinates of each spot or cell. After data preprocessing (see Methods), we extracted the HV genes using the gene expression matrix and the SV genes using both gene expression and spatial coordinates. We use Leiden clustering, a community detection-based clustering method commonly used for clustering transcriptomics data, as our default clustering method (, ). Since our main interest is to cluster