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microbiomeDASim: Simulating longitudinal differential abundance for microbiome data

An increasing emphasis on understanding the dynamics of microbial communities in various settings has led to the proliferation of longitudinal metagenomic sampling studies. Data from whole metagenomic shotgun sequencing and marker-gene survey studies have characteristics that drive novel statistical...

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Detalles Bibliográficos
Autores principales: Williams, Justin, Bravo, Hector Corrada, Tom, Jennifer, Paulson, Joseph Nathaniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000 Research Limited 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7047923/
https://www.ncbi.nlm.nih.gov/pubmed/32148761
http://dx.doi.org/10.12688/f1000research.20660.2
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author Williams, Justin
Bravo, Hector Corrada
Tom, Jennifer
Paulson, Joseph Nathaniel
author_facet Williams, Justin
Bravo, Hector Corrada
Tom, Jennifer
Paulson, Joseph Nathaniel
author_sort Williams, Justin
collection PubMed
description An increasing emphasis on understanding the dynamics of microbial communities in various settings has led to the proliferation of longitudinal metagenomic sampling studies. Data from whole metagenomic shotgun sequencing and marker-gene survey studies have characteristics that drive novel statistical methodological development for estimating time intervals of differential abundance. In designing a study and the frequency of collection prior to a study, one may wish to model the ability to detect an effect, e.g., there may be issues with respect to cost, ease of access, etc. Additionally, while every study is unique, it is possible that in certain scenarios one statistical framework may be more appropriate than another. Here, we present a simulation paradigm implemented in the R Bioconductor software package microbiomeDASim available at http://bioconductor.org/packages/microbiomeDASim microbiomeDASim. microbiomeDASim allows investigators to simulate longitudinal differential abundant microbiome features with a variety of known functional forms with flexible parameters to control desired signal-to-noise ratio. We present metrics of success results on one particular method called metaSplines.
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spelling pubmed-70479232020-03-05 microbiomeDASim: Simulating longitudinal differential abundance for microbiome data Williams, Justin Bravo, Hector Corrada Tom, Jennifer Paulson, Joseph Nathaniel F1000Res Software Tool Article An increasing emphasis on understanding the dynamics of microbial communities in various settings has led to the proliferation of longitudinal metagenomic sampling studies. Data from whole metagenomic shotgun sequencing and marker-gene survey studies have characteristics that drive novel statistical methodological development for estimating time intervals of differential abundance. In designing a study and the frequency of collection prior to a study, one may wish to model the ability to detect an effect, e.g., there may be issues with respect to cost, ease of access, etc. Additionally, while every study is unique, it is possible that in certain scenarios one statistical framework may be more appropriate than another. Here, we present a simulation paradigm implemented in the R Bioconductor software package microbiomeDASim available at http://bioconductor.org/packages/microbiomeDASim microbiomeDASim. microbiomeDASim allows investigators to simulate longitudinal differential abundant microbiome features with a variety of known functional forms with flexible parameters to control desired signal-to-noise ratio. We present metrics of success results on one particular method called metaSplines. F1000 Research Limited 2020-02-26 /pmc/articles/PMC7047923/ /pubmed/32148761 http://dx.doi.org/10.12688/f1000research.20660.2 Text en Copyright: © 2020 Williams J et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Software Tool Article
Williams, Justin
Bravo, Hector Corrada
Tom, Jennifer
Paulson, Joseph Nathaniel
microbiomeDASim: Simulating longitudinal differential abundance for microbiome data
title microbiomeDASim: Simulating longitudinal differential abundance for microbiome data
title_full microbiomeDASim: Simulating longitudinal differential abundance for microbiome data
title_fullStr microbiomeDASim: Simulating longitudinal differential abundance for microbiome data
title_full_unstemmed microbiomeDASim: Simulating longitudinal differential abundance for microbiome data
title_short microbiomeDASim: Simulating longitudinal differential abundance for microbiome data
title_sort microbiomedasim: simulating longitudinal differential abundance for microbiome data
topic Software Tool Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7047923/
https://www.ncbi.nlm.nih.gov/pubmed/32148761
http://dx.doi.org/10.12688/f1000research.20660.2
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