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

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 n...

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Detalles Bibliográficos
Autores principales: Charitakis, Natalie, Salim, Agus, Piers, Adam T., Watt, Kevin I., Porrello, Enzo R., Elliott, David A., Ramialison, Mirana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10506280/
https://www.ncbi.nlm.nih.gov/pubmed/37723583
http://dx.doi.org/10.1186/s13059-023-03045-1
Descripción
Sumario: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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-03045-1.