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AntiSplodge: a neural-network-based RNA-profile deconvolution pipeline designed for spatial transcriptomics

With the current surge of spatial transcriptomics (ST) studies, researchers are exploring the deep interactive cell-play directly in tissues, in situ. However, with the current technologies, measurements consist of mRNA transcript profiles of mixed origin. Recently, applications have been proposed t...

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Autores principales: Lund, Jesper B, Lindberg, Eric L, Maatz, Henrike, Pottbaecker, Fabian, Hübner, Norbert, Lippert, Christoph
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9549785/
https://www.ncbi.nlm.nih.gov/pubmed/36225530
http://dx.doi.org/10.1093/nargab/lqac073
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author Lund, Jesper B
Lindberg, Eric L
Maatz, Henrike
Pottbaecker, Fabian
Hübner, Norbert
Lippert, Christoph
author_facet Lund, Jesper B
Lindberg, Eric L
Maatz, Henrike
Pottbaecker, Fabian
Hübner, Norbert
Lippert, Christoph
author_sort Lund, Jesper B
collection PubMed
description With the current surge of spatial transcriptomics (ST) studies, researchers are exploring the deep interactive cell-play directly in tissues, in situ. However, with the current technologies, measurements consist of mRNA transcript profiles of mixed origin. Recently, applications have been proposed to tackle the deconvolution process, to gain knowledge about which cell types (SC) are found within. This is usually done by incorporating metrics from single-cell (SC) RNA, from similar tissues. Yet, most existing tools are cumbersome, and we found them hard to integrate and properly utilize. Therefore, we present AntiSplodge, a simple feed-forward neural-network-based pipeline designed to effective deconvolute ST profiles by utilizing synthetic ST profiles derived from real-life SC datasets. AntiSplodge is designed to be easy, fast and intuitive while still being lightweight. To demonstrate AntiSplodge, we deconvolute the human heart and verify correctness across time points. We further deconvolute the mouse brain, where spot patterns correctly follow that of the underlying tissue. In particular, for the hippocampus from where the cells originate. Furthermore, AntiSplodge demonstrates top of the line performance when compared to current state-of-the-art tools. Software availability: https://github.com/HealthML/AntiSplodge/.
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spelling pubmed-95497852022-10-11 AntiSplodge: a neural-network-based RNA-profile deconvolution pipeline designed for spatial transcriptomics Lund, Jesper B Lindberg, Eric L Maatz, Henrike Pottbaecker, Fabian Hübner, Norbert Lippert, Christoph NAR Genom Bioinform Methods Article With the current surge of spatial transcriptomics (ST) studies, researchers are exploring the deep interactive cell-play directly in tissues, in situ. However, with the current technologies, measurements consist of mRNA transcript profiles of mixed origin. Recently, applications have been proposed to tackle the deconvolution process, to gain knowledge about which cell types (SC) are found within. This is usually done by incorporating metrics from single-cell (SC) RNA, from similar tissues. Yet, most existing tools are cumbersome, and we found them hard to integrate and properly utilize. Therefore, we present AntiSplodge, a simple feed-forward neural-network-based pipeline designed to effective deconvolute ST profiles by utilizing synthetic ST profiles derived from real-life SC datasets. AntiSplodge is designed to be easy, fast and intuitive while still being lightweight. To demonstrate AntiSplodge, we deconvolute the human heart and verify correctness across time points. We further deconvolute the mouse brain, where spot patterns correctly follow that of the underlying tissue. In particular, for the hippocampus from where the cells originate. Furthermore, AntiSplodge demonstrates top of the line performance when compared to current state-of-the-art tools. Software availability: https://github.com/HealthML/AntiSplodge/. Oxford University Press 2022-10-10 /pmc/articles/PMC9549785/ /pubmed/36225530 http://dx.doi.org/10.1093/nargab/lqac073 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics. 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 Methods Article
Lund, Jesper B
Lindberg, Eric L
Maatz, Henrike
Pottbaecker, Fabian
Hübner, Norbert
Lippert, Christoph
AntiSplodge: a neural-network-based RNA-profile deconvolution pipeline designed for spatial transcriptomics
title AntiSplodge: a neural-network-based RNA-profile deconvolution pipeline designed for spatial transcriptomics
title_full AntiSplodge: a neural-network-based RNA-profile deconvolution pipeline designed for spatial transcriptomics
title_fullStr AntiSplodge: a neural-network-based RNA-profile deconvolution pipeline designed for spatial transcriptomics
title_full_unstemmed AntiSplodge: a neural-network-based RNA-profile deconvolution pipeline designed for spatial transcriptomics
title_short AntiSplodge: a neural-network-based RNA-profile deconvolution pipeline designed for spatial transcriptomics
title_sort antisplodge: a neural-network-based rna-profile deconvolution pipeline designed for spatial transcriptomics
topic Methods Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9549785/
https://www.ncbi.nlm.nih.gov/pubmed/36225530
http://dx.doi.org/10.1093/nargab/lqac073
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