报告题目：Statistical integrative analysis for compositional data

报告时间：2025-03-20  10:30-12:00

报 告 人：占翔  教授（东南大学）

报告地点：雷军科技楼六楼会议室（601）

Abstract：High-dimensional compositional data are frequently encountered nowadays in scientific research of many disciplines, such as in high-throughput sequencing experiments widely used in modern biological and biomedical studies. Statistical analyses with a single compositional dataset have been well studied in the past a few decades in different application contexts, such as regression analysis, clustering analysis, hypothesis testing and so on. However, the inventory of statistical analysis tools for multiple compositional datasets is surprisingly limited, especially in a high-dimensional setting. To fill this research gap, we focus on statistical integrative analysis of multiple compositional datasets in this talk. We first discuss a horizontal integrative analysis, where both predictors and responses are compositional. To investigate associations between two high-dimensional compositional vectors, we propose a Composition-On-Composition regression analysis framework. Then, we introduce our recent vertical integration analysis method that takes two compositional vectors as predictors. Comparing to individual analysis with a single set of compositional predictors, our vertical integration analysis significantly boost the statistical power of association testing between a scalar response variable and two sets of compositional predictors. Superior performance of both integrative analysis methods for multiple compositions are demonstrated through comprehensive numerical simulations studies and real data application examples.