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

ABSTRACT: 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.

SUBMITTER: Charitakis N

PROVIDER: S-EPMC10506280 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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

Charitakis Natalie N Salim Agus A Piers Adam T AT Watt Kevin I KI Porrello Enzo R ER Elliott David A DA Ramialison Mirana M

Genome biology 20230918 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 identificatio ...[more]

PMID: 37723583

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