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MicrobiotaProcess: A comprehensive R package for deep mining microbiome

The data output from microbiome research is growing at an accelerating rate, yet mining the data quickly and efficiently remains difficult. There is still a lack of an effective data structure to represent and manage data, as well as flexible and composable analysis methods. In response to these two...

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Autores principales: Xu, Shuangbin, Zhan, Li, Tang, Wenli, Wang, Qianwen, Dai, Zehan, Zhou, Lang, Feng, Tingze, Chen, Meijun, Wu, Tianzhi, Hu, Erqiang, Yu, Guangchuang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9988672/
https://www.ncbi.nlm.nih.gov/pubmed/36895758
http://dx.doi.org/10.1016/j.xinn.2023.100388
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author Xu, Shuangbin
Zhan, Li
Tang, Wenli
Wang, Qianwen
Dai, Zehan
Zhou, Lang
Feng, Tingze
Chen, Meijun
Wu, Tianzhi
Hu, Erqiang
Yu, Guangchuang
author_facet Xu, Shuangbin
Zhan, Li
Tang, Wenli
Wang, Qianwen
Dai, Zehan
Zhou, Lang
Feng, Tingze
Chen, Meijun
Wu, Tianzhi
Hu, Erqiang
Yu, Guangchuang
author_sort Xu, Shuangbin
collection PubMed
description The data output from microbiome research is growing at an accelerating rate, yet mining the data quickly and efficiently remains difficult. There is still a lack of an effective data structure to represent and manage data, as well as flexible and composable analysis methods. In response to these two issues, we designed and developed the MicrobiotaProcess package. It provides a comprehensive data structure, MPSE, to better integrate the primary and intermediate data, which improves the integration and exploration of the downstream data. Around this data structure, the downstream analysis tasks are decomposed and a set of functions are designed under a tidy framework. These functions independently perform simple tasks and can be combined to perform complex tasks. This gives users the ability to explore data, conduct personalized analyses, and develop analysis workflows. Moreover, MicrobiotaProcess can interoperate with other packages in the R community, which further expands its analytical capabilities. This article demonstrates the MicrobiotaProcess for analyzing microbiome data as well as other ecological data through several examples. It connects upstream data, provides flexible downstream analysis components, and provides visualization methods to assist in presenting and interpreting results.
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spelling pubmed-99886722023-03-08 MicrobiotaProcess: A comprehensive R package for deep mining microbiome Xu, Shuangbin Zhan, Li Tang, Wenli Wang, Qianwen Dai, Zehan Zhou, Lang Feng, Tingze Chen, Meijun Wu, Tianzhi Hu, Erqiang Yu, Guangchuang Innovation (Camb) Article The data output from microbiome research is growing at an accelerating rate, yet mining the data quickly and efficiently remains difficult. There is still a lack of an effective data structure to represent and manage data, as well as flexible and composable analysis methods. In response to these two issues, we designed and developed the MicrobiotaProcess package. It provides a comprehensive data structure, MPSE, to better integrate the primary and intermediate data, which improves the integration and exploration of the downstream data. Around this data structure, the downstream analysis tasks are decomposed and a set of functions are designed under a tidy framework. These functions independently perform simple tasks and can be combined to perform complex tasks. This gives users the ability to explore data, conduct personalized analyses, and develop analysis workflows. Moreover, MicrobiotaProcess can interoperate with other packages in the R community, which further expands its analytical capabilities. This article demonstrates the MicrobiotaProcess for analyzing microbiome data as well as other ecological data through several examples. It connects upstream data, provides flexible downstream analysis components, and provides visualization methods to assist in presenting and interpreting results. Elsevier 2023-02-02 /pmc/articles/PMC9988672/ /pubmed/36895758 http://dx.doi.org/10.1016/j.xinn.2023.100388 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Xu, Shuangbin
Zhan, Li
Tang, Wenli
Wang, Qianwen
Dai, Zehan
Zhou, Lang
Feng, Tingze
Chen, Meijun
Wu, Tianzhi
Hu, Erqiang
Yu, Guangchuang
MicrobiotaProcess: A comprehensive R package for deep mining microbiome
title MicrobiotaProcess: A comprehensive R package for deep mining microbiome
title_full MicrobiotaProcess: A comprehensive R package for deep mining microbiome
title_fullStr MicrobiotaProcess: A comprehensive R package for deep mining microbiome
title_full_unstemmed MicrobiotaProcess: A comprehensive R package for deep mining microbiome
title_short MicrobiotaProcess: A comprehensive R package for deep mining microbiome
title_sort microbiotaprocess: a comprehensive r package for deep mining microbiome
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9988672/
https://www.ncbi.nlm.nih.gov/pubmed/36895758
http://dx.doi.org/10.1016/j.xinn.2023.100388
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