NBZIMM: negative binomial and zero-inflated mixed models, with application to microbiome/metagenomics data analysis
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NBZIMM: negative binomial and zero‑inflated mixed models, with application to microbiome/metagenomics data analysis Xinyan Zhang1 and Nengjun Yi2* *Correspondence: [email protected] 2 Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA Full list of author information is available at the end of the article
Abstract Background: Microbiome/metagenomic data have specific characteristics, including varying total sequence reads, over-dispersion, and zero-inflation, which require tailored analytic tools. Many microbiome/metagenomic studies follow a longitudinal design to collect samples, which further complicates the analysis methods needed. A flexible and efficient R package is needed for analyzing processed multilevel or longitudinal microbiome/metagenomic data. Results: NBZIMM is a freely available R package that provides functions for setting up and fitting negative binomial mixed models, zero-inflated negative binomial mixed models, and zero-inflated Gaussian mixed models. It also provides functions to summarize the results from fitted models, both numerically and graphically. The main functions are built on top of the commonly used R packages nlme and MASS, allowing us to incorporate the well-developed analytic procedures into the framework for analyzing over-dispersed and zero-inflated count or proportion data with multilevel structures (e.g., longitudinal studies). The statistical methods and their implementations in NBZIMM particularly address the data characteristics and the complex designs in microbiome/metagenomic studies. The package is freely available from the public GitHub repository https://github.com/nyiuab/NBZIMM. Conclusion: The NBZIMM package provides useful tools for complex microbiome/ metagenomics data analysis. Keywords: Microbiome, Metagenomics, NBZIMM, Negative binomial mixed models, Zero-inflated mixed models
Background The recent development of technology and computational tools promotes the generation microbiome/metagenomic data, providing research opportunities to identify the links between the microbiome and diseases [1]. 16S rRNA and whole-metagenome shotgun sequencing data are two types of microbiome/metagenomic data available [2, 3]. The downstream bioinformatics pipelines will convert the raw microbiome/metagenomics © The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, y
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