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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
F1000 Research Limited
2020
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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. |
format | Online Article Text |
id | pubmed-7047923 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | F1000 Research Limited |
record_format | MEDLINE/PubMed |
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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