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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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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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. |
format | Online Article Text |
id | pubmed-9988672 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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