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Overcoming false-positive gene-category enrichment in the analysis of spatially resolved transcriptomic brain atlas data
Transcriptomic atlases have improved our understanding of the correlations between gene-expression patterns and spatially varying properties of brain structure and function. Gene-category enrichment analysis (GCEA) is a common method to identify functional gene categories that drive these associatio...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8113439/ https://www.ncbi.nlm.nih.gov/pubmed/33976144 http://dx.doi.org/10.1038/s41467-021-22862-1 |
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author | Fulcher, Ben D. Arnatkeviciute, Aurina Fornito, Alex |
author_facet | Fulcher, Ben D. Arnatkeviciute, Aurina Fornito, Alex |
author_sort | Fulcher, Ben D. |
collection | PubMed |
description | Transcriptomic atlases have improved our understanding of the correlations between gene-expression patterns and spatially varying properties of brain structure and function. Gene-category enrichment analysis (GCEA) is a common method to identify functional gene categories that drive these associations, using gene-to-category annotation systems like the Gene Ontology (GO). Here, we show that applying standard GCEA methodology to spatial transcriptomic data is affected by substantial false-positive bias, with GO categories displaying an over 500-fold average inflation of false-positive associations with random neural phenotypes in mouse and human. The estimated false-positive rate of a GO category is associated with its rate of being reported as significantly enriched in the literature, suggesting that published reports are affected by this false-positive bias. We show that within-category gene–gene coexpression and spatial autocorrelation are key drivers of the false-positive bias and introduce flexible ensemble-based null models that can account for these effects, made available as a software toolbox. |
format | Online Article Text |
id | pubmed-8113439 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-81134392021-05-14 Overcoming false-positive gene-category enrichment in the analysis of spatially resolved transcriptomic brain atlas data Fulcher, Ben D. Arnatkeviciute, Aurina Fornito, Alex Nat Commun Article Transcriptomic atlases have improved our understanding of the correlations between gene-expression patterns and spatially varying properties of brain structure and function. Gene-category enrichment analysis (GCEA) is a common method to identify functional gene categories that drive these associations, using gene-to-category annotation systems like the Gene Ontology (GO). Here, we show that applying standard GCEA methodology to spatial transcriptomic data is affected by substantial false-positive bias, with GO categories displaying an over 500-fold average inflation of false-positive associations with random neural phenotypes in mouse and human. The estimated false-positive rate of a GO category is associated with its rate of being reported as significantly enriched in the literature, suggesting that published reports are affected by this false-positive bias. We show that within-category gene–gene coexpression and spatial autocorrelation are key drivers of the false-positive bias and introduce flexible ensemble-based null models that can account for these effects, made available as a software toolbox. Nature Publishing Group UK 2021-05-11 /pmc/articles/PMC8113439/ /pubmed/33976144 http://dx.doi.org/10.1038/s41467-021-22862-1 Text en © The Author(s) 2021 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 Fulcher, Ben D. Arnatkeviciute, Aurina Fornito, Alex Overcoming false-positive gene-category enrichment in the analysis of spatially resolved transcriptomic brain atlas data |
title | Overcoming false-positive gene-category enrichment in the analysis of spatially resolved transcriptomic brain atlas data |
title_full | Overcoming false-positive gene-category enrichment in the analysis of spatially resolved transcriptomic brain atlas data |
title_fullStr | Overcoming false-positive gene-category enrichment in the analysis of spatially resolved transcriptomic brain atlas data |
title_full_unstemmed | Overcoming false-positive gene-category enrichment in the analysis of spatially resolved transcriptomic brain atlas data |
title_short | Overcoming false-positive gene-category enrichment in the analysis of spatially resolved transcriptomic brain atlas data |
title_sort | overcoming false-positive gene-category enrichment in the analysis of spatially resolved transcriptomic brain atlas data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8113439/ https://www.ncbi.nlm.nih.gov/pubmed/33976144 http://dx.doi.org/10.1038/s41467-021-22862-1 |
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