SPIRAL: integrating and aligning spatially resolved transcriptomics data across different experiments, conditions, and technologies

Properly integrating spatially resolved transcriptomics (SRT) generated from different batches into a unified gene-spatial coordinate system could enable the construction of a comprehensive spatial transcriptome atlas. Here, we propose SPIRAL, consisting of two consecutive modules: SPIRAL-integratio...

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Autores principales: Guo, Tiantian, Yuan, Zhiyuan, Pan, Yan, Wang, Jiakang, Chen, Fengling, Zhang, Michael Q., Li, Xiangyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10590036/
https://www.ncbi.nlm.nih.gov/pubmed/37864231
http://dx.doi.org/10.1186/s13059-023-03078-6
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author Guo, Tiantian
Yuan, Zhiyuan
Pan, Yan
Wang, Jiakang
Chen, Fengling
Zhang, Michael Q.
Li, Xiangyu
author_facet Guo, Tiantian
Yuan, Zhiyuan
Pan, Yan
Wang, Jiakang
Chen, Fengling
Zhang, Michael Q.
Li, Xiangyu
author_sort Guo, Tiantian
collection PubMed
description Properly integrating spatially resolved transcriptomics (SRT) generated from different batches into a unified gene-spatial coordinate system could enable the construction of a comprehensive spatial transcriptome atlas. Here, we propose SPIRAL, consisting of two consecutive modules: SPIRAL-integration, with graph domain adaptation-based data integration, and SPIRAL-alignment, with cluster-aware optimal transport-based coordination alignment. We verify SPIRAL with both synthetic and real SRT datasets. By encoding spatial correlations to gene expressions, SPIRAL-integration surpasses state-of-the-art methods in both batch effect removal and joint spatial domain identification. By aligning spots cluster-wise, SPIRAL-alignment achieves more accurate coordinate alignments than existing methods. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-03078-6.
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spelling pubmed-105900362023-10-22 SPIRAL: integrating and aligning spatially resolved transcriptomics data across different experiments, conditions, and technologies Guo, Tiantian Yuan, Zhiyuan Pan, Yan Wang, Jiakang Chen, Fengling Zhang, Michael Q. Li, Xiangyu Genome Biol Method Properly integrating spatially resolved transcriptomics (SRT) generated from different batches into a unified gene-spatial coordinate system could enable the construction of a comprehensive spatial transcriptome atlas. Here, we propose SPIRAL, consisting of two consecutive modules: SPIRAL-integration, with graph domain adaptation-based data integration, and SPIRAL-alignment, with cluster-aware optimal transport-based coordination alignment. We verify SPIRAL with both synthetic and real SRT datasets. By encoding spatial correlations to gene expressions, SPIRAL-integration surpasses state-of-the-art methods in both batch effect removal and joint spatial domain identification. By aligning spots cluster-wise, SPIRAL-alignment achieves more accurate coordinate alignments than existing methods. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-03078-6. BioMed Central 2023-10-20 /pmc/articles/PMC10590036/ /pubmed/37864231 http://dx.doi.org/10.1186/s13059-023-03078-6 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 Method
Guo, Tiantian
Yuan, Zhiyuan
Pan, Yan
Wang, Jiakang
Chen, Fengling
Zhang, Michael Q.
Li, Xiangyu
SPIRAL: integrating and aligning spatially resolved transcriptomics data across different experiments, conditions, and technologies
title SPIRAL: integrating and aligning spatially resolved transcriptomics data across different experiments, conditions, and technologies
title_full SPIRAL: integrating and aligning spatially resolved transcriptomics data across different experiments, conditions, and technologies
title_fullStr SPIRAL: integrating and aligning spatially resolved transcriptomics data across different experiments, conditions, and technologies
title_full_unstemmed SPIRAL: integrating and aligning spatially resolved transcriptomics data across different experiments, conditions, and technologies
title_short SPIRAL: integrating and aligning spatially resolved transcriptomics data across different experiments, conditions, and technologies
title_sort spiral: integrating and aligning spatially resolved transcriptomics data across different experiments, conditions, and technologies
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10590036/
https://www.ncbi.nlm.nih.gov/pubmed/37864231
http://dx.doi.org/10.1186/s13059-023-03078-6
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