Statistical methods play a pivotal role in analyzing longitudinal microbiome data and integrating multi-omics data to extract meaningful insights from complex microbial communities. This Research Topic aims to highlight the latest advancements in statistical methodologies specifically tailored for longitudinal microbiome data analysis and the integration of multi-omics data. It will also provide a platform to showcase innovative statistical applications and software, and novel scientific findings in the field of microbiome research.
The purpose of this Research Topic is to foster the exchange of knowledge and ideas among statisticians, bioinformaticians, and researchers working on longitudinal microbiome data analysis and microbiome multi-omics data integration. By showcasing state-of-the-art statistical methodologies and innovative applications, this special issue will contribute to the development of novel statistical frameworks, promote software development and sharing, and advance our understanding of the complex dynamics and interactions within microbiome and between microbiome and other omics.
Topics of interest include, but are not limited to:
1. Longitudinal Microbiome Data Analysis:
• Novel statistical inference methods for hypothesis testing and identification of differentially abundant taxa or functions across longitudinal samples.
• Development of statistical models for capturing temporal variations in microbial community composition and diversity.
• Identification of key factors driving longitudinal changes in the microbiome, such as environmental factors, host characteristics, and interventions.
• Statistical approaches for detecting microbial community dynamics at different taxonomic levels or functional categories.
2. Statistical Integration of Microbiome Multi-Omics Data:
• Statistical frameworks for integrating microbiome with other omics data such as metatranscriptomics, metabolomics, and proteomics data to gain a comprehensive understanding of microbial community structure and function.
• Multivariate statistical methods for identifying interaction networks between microbial taxa or functions and other omics profiles.
• Dimension reduction techniques and feature selection methods for integrating high-dimensional microbiome multi-omics data.
• Statistical approaches to incorporating multi-omics data into longitudinal models to capture the dynamic interactions between the microbiome and the paired omics features.
3. Statistical Application, Software, and Tools:
• Innovative statistical applications and novel scientific findings from analyzing longitudinal microbiome data and microbiome multi-omics data.
• Development and validation of user-friendly software packages and computational tools specifically designed for statistical analysis of longitudinal microbiome data and microbiome multi-omics data.
• Visualization techniques and interactive tools for exploring and interpreting longitudinal microbiome data analysis and microbiome multi-omics data analysis results.
Statistical methods play a pivotal role in analyzing longitudinal microbiome data and integrating multi-omics data to extract meaningful insights from complex microbial communities. This Research Topic aims to highlight the latest advancements in statistical methodologies specifically tailored for longitudinal microbiome data analysis and the integration of multi-omics data. It will also provide a platform to showcase innovative statistical applications and software, and novel scientific findings in the field of microbiome research.
The purpose of this Research Topic is to foster the exchange of knowledge and ideas among statisticians, bioinformaticians, and researchers working on longitudinal microbiome data analysis and microbiome multi-omics data integration. By showcasing state-of-the-art statistical methodologies and innovative applications, this special issue will contribute to the development of novel statistical frameworks, promote software development and sharing, and advance our understanding of the complex dynamics and interactions within microbiome and between microbiome and other omics.
Topics of interest include, but are not limited to:
1. Longitudinal Microbiome Data Analysis:
• Novel statistical inference methods for hypothesis testing and identification of differentially abundant taxa or functions across longitudinal samples.
• Development of statistical models for capturing temporal variations in microbial community composition and diversity.
• Identification of key factors driving longitudinal changes in the microbiome, such as environmental factors, host characteristics, and interventions.
• Statistical approaches for detecting microbial community dynamics at different taxonomic levels or functional categories.
2. Statistical Integration of Microbiome Multi-Omics Data:
• Statistical frameworks for integrating microbiome with other omics data such as metatranscriptomics, metabolomics, and proteomics data to gain a comprehensive understanding of microbial community structure and function.
• Multivariate statistical methods for identifying interaction networks between microbial taxa or functions and other omics profiles.
• Dimension reduction techniques and feature selection methods for integrating high-dimensional microbiome multi-omics data.
• Statistical approaches to incorporating multi-omics data into longitudinal models to capture the dynamic interactions between the microbiome and the paired omics features.
3. Statistical Application, Software, and Tools:
• Innovative statistical applications and novel scientific findings from analyzing longitudinal microbiome data and microbiome multi-omics data.
• Development and validation of user-friendly software packages and computational tools specifically designed for statistical analysis of longitudinal microbiome data and microbiome multi-omics data.
• Visualization techniques and interactive tools for exploring and interpreting longitudinal microbiome data analysis and microbiome multi-omics data analysis results.