Benchmarking algorithms for spatially variable gene identification in spatial transcriptomics.

ABSTRACT:

Motivation

The rapid development of spatial transcriptomics has underscored the importance of identifying spatially variable genes. As a fundamental task in spatial transcriptomic data analysis, spatially variable gene identification has been extensively studied. However, the lack of comprehensive benchmark makes it difficult to validate the effectiveness of various algorithms scattered across a large number of studies with real-world datasets.

Results

In response, this article proposes a benchmark framework to evaluate algorithms for identifying spatially variable genes through the analysis of 30 synthesized and 74 real-world datasets, aiming to identify the best algorithms and their corresponding application scenarios. This framework can assist medical and life scientists

SUBMITTER: Chen X 

PROVIDER: S-EPMC12036962 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature