Affiliation:
1. Department of Mathematics, California State University-San Bernardino, San Bernardino, CA 92407, USA
Abstract
Microbiome data is high dimensional, sparse, compositional, and over-dispersed. Therefore, modeling microbiome data is very challenging and it is an active research area. Microbiome analysis has become a progressing area of research as microorganisms constitute a large part of life. Since many methods of microbiome data analysis have been presented, this review summarizes the challenges, methods used, and the advantages and disadvantages of those methods, to serve as an updated guide for those in the field. This review also compared different methods of analysis to progress the development of newer methods.
Subject
General Earth and Planetary Sciences,General Environmental Science
Reference98 articles.
1. Layeghifard, M., Hwang, D.M., and Guttman, D.S. (2018). Microbiome Analysis, Springer.
2. Modeling Sparse Data Using MLE with Applications to Microbiome Data;Aldirawi;J. Stat. Theory Pract.,2022
3. Dousti Mousavi, N., Yang, J., and Aldirawi, H. (2023). Variable Selection for Sparse Data with Applications to Vaginal Microbiome and Gene Expression Data. Genes, 14.
4. The human intestinal microbiome in health and disease;Lynch;N. Engl. J. Med.,2016
5. Microbial interactions: Ecology in a molecular perspective;Braga;Braz. J. Microbiol.,2016
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献