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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.
Afiliação
  • Xu S; Division of Laboratory Medicine, Microbiome Medicine Center, Zhujiang Hospital, Southern Medical University, Guangzhou 510515, China.
  • Zhan L; Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.
  • Tang W; Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.
  • Wang Q; Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.
  • Dai Z; Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.
  • Zhou L; Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.
  • Feng T; Division of Laboratory Medicine, Microbiome Medicine Center, Zhujiang Hospital, Southern Medical University, Guangzhou 510515, China.
  • Chen M; Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.
  • Wu T; Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.
  • Hu E; Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.
  • Yu G; Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.
Innovation (Camb) ; 4(2): 100388, 2023 Mar 13.
Article em En | MEDLINE | ID: mdl-36895758
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.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article