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stVAE deconvolves cell-type composition in large-scale cellular resolution spatial transcriptomics
MOTIVATION: Recent rapid developments in spatial transcriptomic techniques at cellular resolution have gained increasing attention. However, the unique characteristics of large-scale cellular resolution spatial transcriptomic datasets, such as the limited number of transcripts captured per spot and...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10612402/ https://www.ncbi.nlm.nih.gov/pubmed/37862237 http://dx.doi.org/10.1093/bioinformatics/btad642 |
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author | Li, Chen Chan, Ting-Fung Yang, Can Lin, Zhixiang |
author_facet | Li, Chen Chan, Ting-Fung Yang, Can Lin, Zhixiang |
author_sort | Li, Chen |
collection | PubMed |
description | MOTIVATION: Recent rapid developments in spatial transcriptomic techniques at cellular resolution have gained increasing attention. However, the unique characteristics of large-scale cellular resolution spatial transcriptomic datasets, such as the limited number of transcripts captured per spot and the vast number of spots, pose significant challenges to current cell-type deconvolution methods. RESULTS: In this study, we introduce stVAE, a method based on the variational autoencoder framework to deconvolve the cell-type composition of cellular resolution spatial transcriptomic datasets. To assess the performance of stVAE, we apply it to five datasets across three different biological tissues. In the Stereo-seq and Slide-seqV2 datasets of the mouse brain, stVAE accurately reconstructs the laminar structure of the pyramidal cell layers in the cortex, which are mainly organized by the subtypes of telencephalon projecting excitatory neurons. In the Stereo-seq dataset of the E12.5 mouse embryo, stVAE resolves the complex spatial patterns of osteoblast subtypes, which are supported by their marker genes. In Stereo-seq and Pixel-seq datasets of the mouse olfactory bulb, stVAE accurately delineates the spatial distributions of known cell types. In summary, stVAE can accurately identify spatial patterns of cell types and their relative proportions across spots for cellular resolution spatial transcriptomic data. It is instrumental in understanding the heterogeneity of cell populations and their interactions within tissues. AVAILABILITY AND IMPLEMENTATION: stVAE is available in GitHub (https://github.com/lichen2018/stVAE) and Figshare (https://figshare.com/articles/software/stVAE/23254538). |
format | Online Article Text |
id | pubmed-10612402 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-106124022023-10-29 stVAE deconvolves cell-type composition in large-scale cellular resolution spatial transcriptomics Li, Chen Chan, Ting-Fung Yang, Can Lin, Zhixiang Bioinformatics Original Paper MOTIVATION: Recent rapid developments in spatial transcriptomic techniques at cellular resolution have gained increasing attention. However, the unique characteristics of large-scale cellular resolution spatial transcriptomic datasets, such as the limited number of transcripts captured per spot and the vast number of spots, pose significant challenges to current cell-type deconvolution methods. RESULTS: In this study, we introduce stVAE, a method based on the variational autoencoder framework to deconvolve the cell-type composition of cellular resolution spatial transcriptomic datasets. To assess the performance of stVAE, we apply it to five datasets across three different biological tissues. In the Stereo-seq and Slide-seqV2 datasets of the mouse brain, stVAE accurately reconstructs the laminar structure of the pyramidal cell layers in the cortex, which are mainly organized by the subtypes of telencephalon projecting excitatory neurons. In the Stereo-seq dataset of the E12.5 mouse embryo, stVAE resolves the complex spatial patterns of osteoblast subtypes, which are supported by their marker genes. In Stereo-seq and Pixel-seq datasets of the mouse olfactory bulb, stVAE accurately delineates the spatial distributions of known cell types. In summary, stVAE can accurately identify spatial patterns of cell types and their relative proportions across spots for cellular resolution spatial transcriptomic data. It is instrumental in understanding the heterogeneity of cell populations and their interactions within tissues. AVAILABILITY AND IMPLEMENTATION: stVAE is available in GitHub (https://github.com/lichen2018/stVAE) and Figshare (https://figshare.com/articles/software/stVAE/23254538). Oxford University Press 2023-10-20 /pmc/articles/PMC10612402/ /pubmed/37862237 http://dx.doi.org/10.1093/bioinformatics/btad642 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Paper Li, Chen Chan, Ting-Fung Yang, Can Lin, Zhixiang stVAE deconvolves cell-type composition in large-scale cellular resolution spatial transcriptomics |
title | stVAE deconvolves cell-type composition in large-scale cellular resolution spatial transcriptomics |
title_full | stVAE deconvolves cell-type composition in large-scale cellular resolution spatial transcriptomics |
title_fullStr | stVAE deconvolves cell-type composition in large-scale cellular resolution spatial transcriptomics |
title_full_unstemmed | stVAE deconvolves cell-type composition in large-scale cellular resolution spatial transcriptomics |
title_short | stVAE deconvolves cell-type composition in large-scale cellular resolution spatial transcriptomics |
title_sort | stvae deconvolves cell-type composition in large-scale cellular resolution spatial transcriptomics |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10612402/ https://www.ncbi.nlm.nih.gov/pubmed/37862237 http://dx.doi.org/10.1093/bioinformatics/btad642 |
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