SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics

Spatially resolved transcriptomics (SRT)-specific computational methods are often developed, tested, validated, and evaluated in silico using simulated data. Unfortunately, existing simulated SRT data are often poorly documented, hard to reproduce, or unrealistic. Single-cell simulators are not dire...

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Detalles Bibliográficos
Autores principales: Zhu, Jiaqiang, Shang, Lulu, Zhou, Xiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9983268/
https://www.ncbi.nlm.nih.gov/pubmed/36869394
http://dx.doi.org/10.1186/s13059-023-02879-z
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author Zhu, Jiaqiang
Shang, Lulu
Zhou, Xiang
author_facet Zhu, Jiaqiang
Shang, Lulu
Zhou, Xiang
author_sort Zhu, Jiaqiang
collection PubMed
description Spatially resolved transcriptomics (SRT)-specific computational methods are often developed, tested, validated, and evaluated in silico using simulated data. Unfortunately, existing simulated SRT data are often poorly documented, hard to reproduce, or unrealistic. Single-cell simulators are not directly applicable for SRT simulation as they cannot incorporate spatial information. We present SRTsim, an SRT-specific simulator for scalable, reproducible, and realistic SRT simulations. SRTsim not only maintains various expression characteristics of SRT data but also preserves spatial patterns. We illustrate the benefits of SRTsim in benchmarking methods for spatial clustering, spatial expression pattern detection, and cell-cell communication identification. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-02879-z.
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spelling pubmed-99832682023-03-04 SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics Zhu, Jiaqiang Shang, Lulu Zhou, Xiang Genome Biol Method Spatially resolved transcriptomics (SRT)-specific computational methods are often developed, tested, validated, and evaluated in silico using simulated data. Unfortunately, existing simulated SRT data are often poorly documented, hard to reproduce, or unrealistic. Single-cell simulators are not directly applicable for SRT simulation as they cannot incorporate spatial information. We present SRTsim, an SRT-specific simulator for scalable, reproducible, and realistic SRT simulations. SRTsim not only maintains various expression characteristics of SRT data but also preserves spatial patterns. We illustrate the benefits of SRTsim in benchmarking methods for spatial clustering, spatial expression pattern detection, and cell-cell communication identification. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-02879-z. BioMed Central 2023-03-03 /pmc/articles/PMC9983268/ /pubmed/36869394 http://dx.doi.org/10.1186/s13059-023-02879-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 Method
Zhu, Jiaqiang
Shang, Lulu
Zhou, Xiang
SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics
title SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics
title_full SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics
title_fullStr SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics
title_full_unstemmed SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics
title_short SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics
title_sort srtsim: spatial pattern preserving simulations for spatially resolved transcriptomics
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9983268/
https://www.ncbi.nlm.nih.gov/pubmed/36869394
http://dx.doi.org/10.1186/s13059-023-02879-z
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