Original title: Evaluating spatially variable gene detection methods for spatial transcriptomics data
Authors: Carissa Chen,Jieun Hani Kim,Pengyi Yang
In this article, a group of researchers conducted a study to evaluate different methods for identifying spatially variable genes (SVGs) in spatial transcriptomics data. Identifying these genes is important for understanding how genes vary across different regions of tissues and cells. The researchers wanted to know if different methods for detecting SVGs would select the same genes, how reliable the statistical significance from each method was, and how accurate and robust each method was in detecting SVGs. They also considered practical factors such as computational time and memory usage. The researchers evaluated several popular SVG detection methods using a large collection of spatial transcriptomics datasets that included different tissue types, biotechnologies, and spatial resolutions. Their results showed differences among the methods, particularly in calling statistically significant SVGs across datasets. Overall, this study provides valuable considerations for choosing methods to identify SVGs and can serve as a reference for future development in this area.
Original article: https://www.biorxiv.org/content/10.1101/2022.11.23.517747v2