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Applications and Comparison of Dimensionality Reduction Methods for Microbiome Data
Dimensionality reduction techniques are a key component of most microbiome studies, providing both the ability to tractably visualize complex microbiome datasets and the starting point for additional, more formal, statistical analyses. In this review, we discuss the motivation for applying dimension...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9580878/ https://www.ncbi.nlm.nih.gov/pubmed/36304280 http://dx.doi.org/10.3389/fbinf.2022.821861 |
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author | Armstrong, George Rahman, Gibraan Martino, Cameron McDonald, Daniel Gonzalez, Antonio Mishne, Gal Knight, Rob |
author_facet | Armstrong, George Rahman, Gibraan Martino, Cameron McDonald, Daniel Gonzalez, Antonio Mishne, Gal Knight, Rob |
author_sort | Armstrong, George |
collection | PubMed |
description | Dimensionality reduction techniques are a key component of most microbiome studies, providing both the ability to tractably visualize complex microbiome datasets and the starting point for additional, more formal, statistical analyses. In this review, we discuss the motivation for applying dimensionality reduction techniques, the special characteristics of microbiome data such as sparsity and compositionality that make this difficult, the different categories of strategies that are available for dimensionality reduction, and examples from the literature of how they have been successfully applied (together with pitfalls to avoid). We conclude by describing the need for further development in the field, in particular combining the power of phylogenetic analysis with the ability to handle sparsity, compositionality, and non-normality, as well as discussing current techniques that should be applied more widely in future analyses. |
format | Online Article Text |
id | pubmed-9580878 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95808782022-10-26 Applications and Comparison of Dimensionality Reduction Methods for Microbiome Data Armstrong, George Rahman, Gibraan Martino, Cameron McDonald, Daniel Gonzalez, Antonio Mishne, Gal Knight, Rob Front Bioinform Bioinformatics Dimensionality reduction techniques are a key component of most microbiome studies, providing both the ability to tractably visualize complex microbiome datasets and the starting point for additional, more formal, statistical analyses. In this review, we discuss the motivation for applying dimensionality reduction techniques, the special characteristics of microbiome data such as sparsity and compositionality that make this difficult, the different categories of strategies that are available for dimensionality reduction, and examples from the literature of how they have been successfully applied (together with pitfalls to avoid). We conclude by describing the need for further development in the field, in particular combining the power of phylogenetic analysis with the ability to handle sparsity, compositionality, and non-normality, as well as discussing current techniques that should be applied more widely in future analyses. Frontiers Media S.A. 2022-02-24 /pmc/articles/PMC9580878/ /pubmed/36304280 http://dx.doi.org/10.3389/fbinf.2022.821861 Text en Copyright © 2022 Armstrong, Rahman, Martino, McDonald, Gonzalez, Mishne and Knight. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Bioinformatics Armstrong, George Rahman, Gibraan Martino, Cameron McDonald, Daniel Gonzalez, Antonio Mishne, Gal Knight, Rob Applications and Comparison of Dimensionality Reduction Methods for Microbiome Data |
title | Applications and Comparison of Dimensionality Reduction Methods for Microbiome Data |
title_full | Applications and Comparison of Dimensionality Reduction Methods for Microbiome Data |
title_fullStr | Applications and Comparison of Dimensionality Reduction Methods for Microbiome Data |
title_full_unstemmed | Applications and Comparison of Dimensionality Reduction Methods for Microbiome Data |
title_short | Applications and Comparison of Dimensionality Reduction Methods for Microbiome Data |
title_sort | applications and comparison of dimensionality reduction methods for microbiome data |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9580878/ https://www.ncbi.nlm.nih.gov/pubmed/36304280 http://dx.doi.org/10.3389/fbinf.2022.821861 |
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