A0491
Title: Statistical methods applied in microbial metagenomics
Authors: Yunliang Li - University of Saskatchewan (Canada) [presenting]
Abstract: The microbial community plays a pivotal role in influencing human health, environmental sustainability, and ecosystem resilience. Advances in next-generation sequencing techniques have enabled the capture of vast metagenomic information on uncultured microbiota. Leveraging metagenomic sequencing data, researchers can delve into the complex composition of microbial communities and explore the functions of community members in relation to host and environment. However, microbiome data exhibits distinct characteristics, including high dimensionality, sparsity with numerous zero counts, and compositional nature, which present substantial challenges for the application of traditional statistical methods. To tackle these issues, novel statistical approaches are under development to facilitate more effective data analysis and the generation of reproducible and stable conclusions. Statistical methods tailored to various analytical objectives are introduced within microbiome data analysis, encompassing microbial diversity analysis, differentially abundant feature analysis, and microbial interaction analysis. Insights are gained into the strengths and limitations of these methods, empowering them to effectively employ these methods in microbiome data analysis.
