主题：Powerful Large-scale Inference in Omics Association Studies

报告人：Prof. Xianyang ZHANG, Associate Professor, Department of Statistics, Texas A&M University

主持人：Prof. Yunyi ZHANG, Assistant Professor, School of Data Science, CUHK-Shenzhen

日期：28 September (Wednesday), 2022

时间：9:00 to 10:00 AM, Beijing Time

形式：Hybrid 

线下地点：103 Meeting Room, Daoyuan Building 

Zoom链接：https://cuhk-edu-cn.zoom.us/j/5304767369?pwd=aFErUGFSSDlLNWJld0VNNmpTL0k0UT09

Zoom会议号：5304767369

密码：852648

语言：English

摘要：

Increasing statistical power in analyzing omics data through methodological innovation is of tremendous benefit to the field due to resource constraints for individual studies. One direction of innovation is to utilize auxiliary data that could inform us of the statistical and biological properties of the omics features. The first part of the talk will introduce a new multiple testing procedure that can incorporate these auxiliary data to boost the statistical power. We develop a fast algorithm to implement the proposed procedure and prove its asymptotic validity. Through numerical studies, we demonstrate that the new approach improves over the state-of-the-art methods by being flexible, robust, powerful, and computationally efficient. The second part of the talk tackles the issue of statistical power loss when simultaneously adjusting for confounders and multiple testing in omics association studies. The traditional statistical procedure involves fitting a confounder-adjusted regression model for each omics feature, followed by multiple testing correction. Here we show that the conventional procedure is not optimal and present a new approach, 2dFDR, a two-dimensional false discovery rate control procedure, for powerful confounder adjustment in multiple testing. Through extensive evaluation, we show that 2dFDR is more powerful than the traditional procedure, and in the presence of strong confounding and weak signals, the power improvement could be more than 100%.

简介：

Xianyang Zhang is an Associate Professor in the statistics department at Texas A&M University. He obtained his Ph.D. in statistics from the University of Illinois at Urbana Champaign in 2013. His research interests include high dimensional/large-scale statistical inference, kernel methods, genomics data analysis, functional data analysis, time series, and econometrics. He currently serves as an associate editor for Biometrics and Journal of Multivariate Analysis.

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