Model-based Bicluster Algorithm for Microbiome
报告人：刘芃（Iowa State University）
时间：2024-06-13 14:00-15:00
地点：智华楼四元厅-225
Abstract:
With the advancement of next-sequencing technologies, huge amounts of microbiome data have become available. Bicluster analysis is a tool to quantitively explore the relationships between microbial samples and between features simultaneously, and aims to reveal the interactions between microbial sub-communities. It is challenging to conduct bicluster analysis with microbiome data due to compositionality and sparsity. In this talk, we propose a Dirichlet-Multinomial (DM) model-based checkerboard biclustering method to cluster microbiome features and samples simultaneously. This method assumes a mixture of DM distributions across microbiome samples, and uses a combination of Expectation-Maximization algorithm and coordinate descent algorithm to solve for parameter estimates and achieve biclustering results. Simulation studies under a variety of settings show that our proposed method outperforms alternative methods. Application to a real dataset demonstrates the effectiveness of our proposed method and provides interesting biological findings.
About the Speaker:
Peng Liu (刘芃) got her Bachelor of Medicine (Medical Degree in China) from Peking University Health Science Center, her M.S. in Nutritional Sciences from Cornell University, and her Ph.D. in Biological Statistics and Computational Biology from Cornell University. She is currently a Professor in the Department of Statistics at Iowa State University. Her statistical research focuses on statistical genetics and genomics, especially next-generation sequencing data analysis such as microbiome data analysis and multi-omics data analysis. She has published over 70 peer-reviewed publications, advised 18 PhD students and 9 MS students, and served as PI or co-PI for many grants, including 14 federal agency-funded awards from the National Institute of Health (NIH), the United States Department of Agriculture (USDA), the National Science Foundation (NSF), and the Department of Energy (DOE) in USA.
