Evaluating spatially variable gene detection methods for spatial transcriptomics data.

ABSTRACT:

Background

The identification of genes that vary across spatial domains in tissues and cells is an essential step for spatial transcriptomics data analysis. Given the critical role it serves for downstream data interpretations, various methods for detecting spatially variable genes (SVGs) have been proposed. However, the lack of benchmarking complicates the selection of a suitable method.

Results

Here we systematically evaluate a panel of popular SVG detection methods on a large collection of spatial transcriptomics datasets, covering various tissue types, biotechnologies, and spatial resolutions. We address questions including whether different methods select a similar set of SVGs, how reliable is the reported statistical significance from each method, how accurate and robu

SUBMITTER: Chen C 

PROVIDER: S-EPMC10789051 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature