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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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:
Short Report
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
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author Charitakis, Natalie
Salim, Agus
Piers, Adam T.
Watt, Kevin I.
Porrello, Enzo R.
Elliott, David A.
Ramialison, Mirana
author_facet Charitakis, Natalie
Salim, Agus
Piers, Adam T.
Watt, Kevin I.
Porrello, Enzo R.
Elliott, David A.
Ramialison, Mirana
author_sort Charitakis, Natalie
collection PubMed
description 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.
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spelling pubmed-105062802023-09-19 Disparities in spatially variable gene calling highlight the need for benchmarking spatial transcriptomics methods Charitakis, Natalie Salim, Agus Piers, Adam T. Watt, Kevin I. Porrello, Enzo R. Elliott, David A. Ramialison, Mirana Genome Biol Short Report 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. BioMed Central 2023-09-18 /pmc/articles/PMC10506280/ /pubmed/37723583 http://dx.doi.org/10.1186/s13059-023-03045-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Short Report
Charitakis, Natalie
Salim, Agus
Piers, Adam T.
Watt, Kevin I.
Porrello, Enzo R.
Elliott, David A.
Ramialison, Mirana
Disparities in spatially variable gene calling highlight the need for benchmarking spatial transcriptomics methods
title Disparities in spatially variable gene calling highlight the need for benchmarking spatial transcriptomics methods
title_full Disparities in spatially variable gene calling highlight the need for benchmarking spatial transcriptomics methods
title_fullStr Disparities in spatially variable gene calling highlight the need for benchmarking spatial transcriptomics methods
title_full_unstemmed Disparities in spatially variable gene calling highlight the need for benchmarking spatial transcriptomics methods
title_short Disparities in spatially variable gene calling highlight the need for benchmarking spatial transcriptomics methods
title_sort disparities in spatially variable gene calling highlight the need for benchmarking spatial transcriptomics methods
topic Short Report
url 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
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