TITLE: Recent Development in Analyzing Microbiome Data from Clinical Trials
SPEAKER: Yinglin Xia, University of Illinois at Chicago and Din Chen, UNC-Chapel Hill
Moderator: Walter R. Young
This tutorial is based on the book: “Statistical Analysis of Microbiome Data with R” co-authored by Yinglin Xia, Jun Sun and Ding-Geng Chen, published by Springer ICSA Series in Statistics 2018, which uses R to analyze microbiome data from biopharmaceutical clinical trials and biomedical applications. This tutorial provides a thorough presentation of biostatistical analyses of microbiome data with detailed step-by-step illustrations on their implementation using R. Examples of microbiome clinical trial data are based on the authors’ microbiome studies and publicly available datasets in human therapeutic areas such as anti–programmed cell death 1 protein (PD-1) immunotherapy in melanoma cancer patients, anti-TNF in IBD patients. After understanding the application, various biostatistical methods appropriate for analyzing data from microbiome clinical trials are identified. Then statistical programming code is developed using appropriate R packages to analyze the data. The code development and results are presented in a stepwise fashion. This stepwise approach should enable attendees to follow the logic and gain an understanding of the analysis methods and the R implementation so that they may use R to analyze their own microbiome data.
Topics To Be Covered:
- Overview the next-generation sequencing techniques: 16S rRNA and Shotgun metagenomics approaches.
- Illustrate the microbiome clinical trial data structure and features using our microbiome studies and real trials using anti–programmed cell death 1 protein (PD-1) immunotherapy in melanoma cancer patients, anti-TNF in IBD patients, and describe the challenges of analyzing microbiome data.
- Exploratory analysis of microbiome data. We will illustrate several most widely used ordination techniques in microbiome studies, such as principal components analysis (PCA), principal coordinate analysis (PCoA), constrained correspondence analysis (CCA).
- Univariate and multivariate community analyses. We will cover some standard statistical methods such as Kruskal-Wallis test and specially designed methods for microbial ecology such as Permutational MANOVA, analysis of similarity (ANOSIM).
- Compositional analysis of microbiome data. We will start to introduce why microbiome data are compositional and illustrate group comparisons using a compositional package ALDEx2.
- Modeling over-dispersed microbiome data. We will illustrate analyzing microbiome data using edgeR and DESeq2 packages.
- Modeling zero-inflated microbiome data. We will illustrate how to use standard zero-inflated and zero-hurdle models to analyze microbiome data and also describe their limitations.
- Longitudinal microbiome data analysis. We will illustrate how to use zero-inflated beta regression model (ZIBR) to analyze longitudinal microbiome data.
Dr. Yinglin Xia is a Research Associate Professor at the Department of Medicine, the University of Illinois at Chicago, USA. He was a Research Assistant Professor in the Department of Biostatistics and Computational Biology at the University of Rochester, Rochester, NY and was a clinical statistician at Abbvie Inc, North Chicago, IL. Dr. Xia has worked on a variety of research projects and clinical trials in microbiome, gastroenterology, oncology, immunology, psychiatry, sleep, neuroscience, HIV, mental health, public health, social and behavioral sciences, as well as nursing caregiver. He has published more than 100 papers in peer-reviewed journals on Statistical Methodology, Clinical Trial, Medical Statistics, Biomedical Sciences, and Social and Behavioral sciences. He serves the editorial board for several scientific journals. He has successfully applied his statistical knowledge, modeling and programming skills to study designs and data analysis in biomedical research, clinical trials, and in microbiome research. He has written the first book, an invited review, and a book chapter on statistical analysis of microbiome data. He has designed four grants on microbiome studies funded by NIH, VA, and other funding agencies. His recent papers on microbiome data analysis are well received by peers.
Dr. Din Chen is now the Wallace H. Kuralt distinguished professor in Biostatistics at University of North Carolina-Chapel Hill. Before this, Dr. Chen was a professor in biostatistics at the University of Rochester Medical Center, the Karl E. Peace endowed eminent scholar chair and professor in biostatistics from the Jiann-Ping Hsu College of Public Health at the Georgia Southern University. Dr. Chen is an elected fellow of American Statistical Association (ASA), an elected member of the International Statistics Institute (ISI) and a senior expert consultant for biopharmaceuticals and government agencies with extensive expertise in clinical trial biostatistics. Dr. Chen has more than 200 referred professional publications and co-authored/co-edited 30 books on biostatistics clinical trials, biopharmaceutical statistics, interval-censored survival data analysis, meta-analysis, public health statistics, statistical causal inferences; statistical methods in big-data sciences and Monte-Carlo simulation based statistical modeling. Dr. Chen has been invited nationally and internationally to speak and give short courses at various scientific conferences. In fact, Dr. Chen is a committee member of the Deming Conference and has been invited to give various tutorials at Deming Conference since 2011.
Both speakers have extensive experience in biostatistical and clinical trial methodological development and consulting to government and biopharmaceutical industries in SAS and R.