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Comprehensive evaluation of deconvolution methods for human brain gene expression
Transcriptome deconvolution aims to estimate the cellular composition of an RNA sample from its gene expression data, which in turn can be used to correct for composition differences across samples. The human brain is unique in its transcriptomic diversity, and comprises a complex mixture of cell-ty...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8924248/ https://www.ncbi.nlm.nih.gov/pubmed/35292647 http://dx.doi.org/10.1038/s41467-022-28655-4 |
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author | Sutton, Gavin J. Poppe, Daniel Simmons, Rebecca K. Walsh, Kieran Nawaz, Urwah Lister, Ryan Gagnon-Bartsch, Johann A. Voineagu, Irina |
author_facet | Sutton, Gavin J. Poppe, Daniel Simmons, Rebecca K. Walsh, Kieran Nawaz, Urwah Lister, Ryan Gagnon-Bartsch, Johann A. Voineagu, Irina |
author_sort | Sutton, Gavin J. |
collection | PubMed |
description | Transcriptome deconvolution aims to estimate the cellular composition of an RNA sample from its gene expression data, which in turn can be used to correct for composition differences across samples. The human brain is unique in its transcriptomic diversity, and comprises a complex mixture of cell-types, including transcriptionally similar subtypes of neurons. Here, we carry out a comprehensive evaluation of deconvolution methods for human brain transcriptome data, and assess the tissue-specificity of our key observations by comparison with human pancreas and heart. We evaluate eight transcriptome deconvolution approaches and nine cell-type signatures, testing the accuracy of deconvolution using in silico mixtures of single-cell RNA-seq data, RNA mixtures, as well as nearly 2000 human brain samples. Our results identify the main factors that drive deconvolution accuracy for brain data, and highlight the importance of biological factors influencing cell-type signatures, such as brain region and in vitro cell culturing. |
format | Online Article Text |
id | pubmed-8924248 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-89242482022-04-01 Comprehensive evaluation of deconvolution methods for human brain gene expression Sutton, Gavin J. Poppe, Daniel Simmons, Rebecca K. Walsh, Kieran Nawaz, Urwah Lister, Ryan Gagnon-Bartsch, Johann A. Voineagu, Irina Nat Commun Article Transcriptome deconvolution aims to estimate the cellular composition of an RNA sample from its gene expression data, which in turn can be used to correct for composition differences across samples. The human brain is unique in its transcriptomic diversity, and comprises a complex mixture of cell-types, including transcriptionally similar subtypes of neurons. Here, we carry out a comprehensive evaluation of deconvolution methods for human brain transcriptome data, and assess the tissue-specificity of our key observations by comparison with human pancreas and heart. We evaluate eight transcriptome deconvolution approaches and nine cell-type signatures, testing the accuracy of deconvolution using in silico mixtures of single-cell RNA-seq data, RNA mixtures, as well as nearly 2000 human brain samples. Our results identify the main factors that drive deconvolution accuracy for brain data, and highlight the importance of biological factors influencing cell-type signatures, such as brain region and in vitro cell culturing. Nature Publishing Group UK 2022-03-15 /pmc/articles/PMC8924248/ /pubmed/35292647 http://dx.doi.org/10.1038/s41467-022-28655-4 Text en © The Author(s) 2022 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Sutton, Gavin J. Poppe, Daniel Simmons, Rebecca K. Walsh, Kieran Nawaz, Urwah Lister, Ryan Gagnon-Bartsch, Johann A. Voineagu, Irina Comprehensive evaluation of deconvolution methods for human brain gene expression |
title | Comprehensive evaluation of deconvolution methods for human brain gene expression |
title_full | Comprehensive evaluation of deconvolution methods for human brain gene expression |
title_fullStr | Comprehensive evaluation of deconvolution methods for human brain gene expression |
title_full_unstemmed | Comprehensive evaluation of deconvolution methods for human brain gene expression |
title_short | Comprehensive evaluation of deconvolution methods for human brain gene expression |
title_sort | comprehensive evaluation of deconvolution methods for human brain gene expression |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8924248/ https://www.ncbi.nlm.nih.gov/pubmed/35292647 http://dx.doi.org/10.1038/s41467-022-28655-4 |
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