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spSeudoMap: cell type mapping of spatial transcriptomics using unmatched single-cell RNA-seq data
Since many single-cell RNA-seq (scRNA-seq) data are obtained after cell sorting, such as when investigating immune cells, tracking cellular landscape by integrating single-cell data with spatial transcriptomic data is limited due to cell type and cell composition mismatch between the two datasets. W...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10021938/ https://www.ncbi.nlm.nih.gov/pubmed/36932388 http://dx.doi.org/10.1186/s13073-023-01168-5 |
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author | Bae, Sungwoo Choi, Hongyoon Lee, Dong Soo |
author_facet | Bae, Sungwoo Choi, Hongyoon Lee, Dong Soo |
author_sort | Bae, Sungwoo |
collection | PubMed |
description | Since many single-cell RNA-seq (scRNA-seq) data are obtained after cell sorting, such as when investigating immune cells, tracking cellular landscape by integrating single-cell data with spatial transcriptomic data is limited due to cell type and cell composition mismatch between the two datasets. We developed a method, spSeudoMap, which utilizes sorted scRNA-seq data to create virtual cell mixtures that closely mimic the gene expression of spatial data and trains a domain adaptation model for predicting spatial cell compositions. The method was applied in brain and breast cancer tissues and accurately predicted the topography of cell subpopulations. spSeudoMap may help clarify the roles of a few, but crucial cell types. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13073-023-01168-5. |
format | Online Article Text |
id | pubmed-10021938 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-100219382023-03-18 spSeudoMap: cell type mapping of spatial transcriptomics using unmatched single-cell RNA-seq data Bae, Sungwoo Choi, Hongyoon Lee, Dong Soo Genome Med Method Since many single-cell RNA-seq (scRNA-seq) data are obtained after cell sorting, such as when investigating immune cells, tracking cellular landscape by integrating single-cell data with spatial transcriptomic data is limited due to cell type and cell composition mismatch between the two datasets. We developed a method, spSeudoMap, which utilizes sorted scRNA-seq data to create virtual cell mixtures that closely mimic the gene expression of spatial data and trains a domain adaptation model for predicting spatial cell compositions. The method was applied in brain and breast cancer tissues and accurately predicted the topography of cell subpopulations. spSeudoMap may help clarify the roles of a few, but crucial cell types. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13073-023-01168-5. BioMed Central 2023-03-17 /pmc/articles/PMC10021938/ /pubmed/36932388 http://dx.doi.org/10.1186/s13073-023-01168-5 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 Bae, Sungwoo Choi, Hongyoon Lee, Dong Soo spSeudoMap: cell type mapping of spatial transcriptomics using unmatched single-cell RNA-seq data |
title | spSeudoMap: cell type mapping of spatial transcriptomics using unmatched single-cell RNA-seq data |
title_full | spSeudoMap: cell type mapping of spatial transcriptomics using unmatched single-cell RNA-seq data |
title_fullStr | spSeudoMap: cell type mapping of spatial transcriptomics using unmatched single-cell RNA-seq data |
title_full_unstemmed | spSeudoMap: cell type mapping of spatial transcriptomics using unmatched single-cell RNA-seq data |
title_short | spSeudoMap: cell type mapping of spatial transcriptomics using unmatched single-cell RNA-seq data |
title_sort | spseudomap: cell type mapping of spatial transcriptomics using unmatched single-cell rna-seq data |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10021938/ https://www.ncbi.nlm.nih.gov/pubmed/36932388 http://dx.doi.org/10.1186/s13073-023-01168-5 |
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