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 issues, we designed and developed the MicrobiotaProcess package. It provides a comprehensive data structure, MPSE, to better integrate the primary and in-termediate 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 work-flows. 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 up-stream data, provides flexible downstream analysis components, and pro-vides visualization methods to assist in presenting and interpreting results.

Automated summary

The data output from microbiome research is growing rapidly, but efficiently mining the data remains a challenge. To address this issue, the MicrobiotaProcess package was designed and developed, providing a comprehensive data structure and a set of functions for downstream analysis. This package enables users to explore and analyze data, develop personalized workflows, and integrates with other packages in the R community. This article demonstrates the application of MicrobiotaProcess in analyzing microbiome and ecological data through examples, offering data integration, flexible analysis components, and visualization methods for result interpretation.