Tree-aggregated predictive modeling of microbiome data

Abstract Modern high-throughput sequencing technologies provide low-cost microbiome survey data across all habitats of life at unprecedented scale. At the most granular level, the primary data consist of sparse counts of amplicon sequence variants or operational taxonomic units that are associated w...

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Autores principales:	Jacob Bien, Xiaohan Yan, Léo Simpson, Christian L. Müller
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6bc1f533b34c4f359185a96658f9e0d0
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spelling	oai:doaj.org-article:6bc1f533b34c4f359185a96658f9e0d02021-12-02T16:08:06ZTree-aggregated predictive modeling of microbiome data10.1038/s41598-021-93645-32045-2322https://doaj.org/article/6bc1f533b34c4f359185a96658f9e0d02021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93645-3https://doaj.org/toc/2045-2322Abstract Modern high-throughput sequencing technologies provide low-cost microbiome survey data across all habitats of life at unprecedented scale. At the most granular level, the primary data consist of sparse counts of amplicon sequence variants or operational taxonomic units that are associated with taxonomic and phylogenetic group information. In this contribution, we leverage the hierarchical structure of amplicon data and propose a data-driven and scalable tree-guided aggregation framework to associate microbial subcompositions with response variables of interest. The excess number of zero or low count measurements at the read level forces traditional microbiome data analysis workflows to remove rare sequencing variants or group them by a fixed taxonomic rank, such as genus or phylum, or by phylogenetic similarity. By contrast, our framework, which we call trac (tree-aggregation of compositional data), learns data-adaptive taxon aggregation levels for predictive modeling, greatly reducing the need for user-defined aggregation in preprocessing while simultaneously integrating seamlessly into the compositional data analysis framework. We illustrate the versatility of our framework in the context of large-scale regression problems in human gut, soil, and marine microbial ecosystems. We posit that the inferred aggregation levels provide highly interpretable taxon groupings that can help microbiome researchers gain insights into the structure and functioning of the underlying ecosystem of interest.Jacob BienXiaohan YanLéo SimpsonChristian L. MüllerNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jacob Bien Xiaohan Yan Léo Simpson Christian L. Müller Tree-aggregated predictive modeling of microbiome data
description	Abstract Modern high-throughput sequencing technologies provide low-cost microbiome survey data across all habitats of life at unprecedented scale. At the most granular level, the primary data consist of sparse counts of amplicon sequence variants or operational taxonomic units that are associated with taxonomic and phylogenetic group information. In this contribution, we leverage the hierarchical structure of amplicon data and propose a data-driven and scalable tree-guided aggregation framework to associate microbial subcompositions with response variables of interest. The excess number of zero or low count measurements at the read level forces traditional microbiome data analysis workflows to remove rare sequencing variants or group them by a fixed taxonomic rank, such as genus or phylum, or by phylogenetic similarity. By contrast, our framework, which we call trac (tree-aggregation of compositional data), learns data-adaptive taxon aggregation levels for predictive modeling, greatly reducing the need for user-defined aggregation in preprocessing while simultaneously integrating seamlessly into the compositional data analysis framework. We illustrate the versatility of our framework in the context of large-scale regression problems in human gut, soil, and marine microbial ecosystems. We posit that the inferred aggregation levels provide highly interpretable taxon groupings that can help microbiome researchers gain insights into the structure and functioning of the underlying ecosystem of interest.
format	article
author	Jacob Bien Xiaohan Yan Léo Simpson Christian L. Müller
author_facet	Jacob Bien Xiaohan Yan Léo Simpson Christian L. Müller
author_sort	Jacob Bien
title	Tree-aggregated predictive modeling of microbiome data
title_short	Tree-aggregated predictive modeling of microbiome data
title_full	Tree-aggregated predictive modeling of microbiome data
title_fullStr	Tree-aggregated predictive modeling of microbiome data
title_full_unstemmed	Tree-aggregated predictive modeling of microbiome data
title_sort	tree-aggregated predictive modeling of microbiome data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6bc1f533b34c4f359185a96658f9e0d0
work_keys_str_mv	AT jacobbien treeaggregatedpredictivemodelingofmicrobiomedata AT xiaohanyan treeaggregatedpredictivemodelingofmicrobiomedata AT leosimpson treeaggregatedpredictivemodelingofmicrobiomedata AT christianlmuller treeaggregatedpredictivemodelingofmicrobiomedata
_version_	1718384602368704512

Tree-aggregated predictive modeling of microbiome data

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