Nevin Manimala Statistics

Disparities in spatially variable gene calling highlight the need for benchmarking spatial transcriptomics methods

Genome Biol. 2023 Sep 18;24(1):209. doi: 10.1186/s13059-023-03045-1.


Identifying spatially variable genes (SVGs) is a key step in the analysis of spatially resolved transcriptomics data. SVGs provide biological insights by defining transcriptomic differences within tissues, which was previously unachievable using RNA-sequencing technologies. However, the increasing number of published tools designed to define SVG sets currently lack benchmarking methods to accurately assess performance. This study compares results of 6 purpose-built packages for SVG identification across 9 public and 5 simulated datasets and highlights discrepancies between results. Additional tools for generation of simulated data and development of benchmarking methods are required to improve methods for identifying SVGs.

PMID:37723583 | DOI:10.1186/s13059-023-03045-1

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