Cargando…
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...
Autores principales: | , , , , , |
---|---|
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 |
_version_ | 1784805747740114944 |
---|---|
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/. |
format | Online Article Text |
id | pubmed-9549785 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT lundjesperb antisplodgeaneuralnetworkbasedrnaprofiledeconvolutionpipelinedesignedforspatialtranscriptomics AT lindbergericl antisplodgeaneuralnetworkbasedrnaprofiledeconvolutionpipelinedesignedforspatialtranscriptomics AT maatzhenrike antisplodgeaneuralnetworkbasedrnaprofiledeconvolutionpipelinedesignedforspatialtranscriptomics AT pottbaeckerfabian antisplodgeaneuralnetworkbasedrnaprofiledeconvolutionpipelinedesignedforspatialtranscriptomics AT hubnernorbert antisplodgeaneuralnetworkbasedrnaprofiledeconvolutionpipelinedesignedforspatialtranscriptomics AT lippertchristoph antisplodgeaneuralnetworkbasedrnaprofiledeconvolutionpipelinedesignedforspatialtranscriptomics |