Systematic benchmarking of computational methods to identify spatially variable genes.

ABSTRACT:

Background

Spatially resolved transcriptomics offers unprecedented insight by enabling the profiling of gene expression within the intact spatial context of cells, effectively adding a new and essential dimension to data interpretation. To efficiently detect spatial structure of interest, an essential step in analyzing such data involves identifying spatially variable genes (SVGs). Despite researchers having developed several computational methods to accomplish this task, the lack of a comprehensive benchmark evaluating their performance remains a considerable gap in the field.

Results

Here, we systematically evaluate 14 methods using 96 spatial datasets and 6 metrics. We compare the methods regarding gene ranking and classification based on real spatial variation, statistic

SUBMITTER: Li Z 

PROVIDER: S-EPMC12445034 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature