A powerful empirical Bayes approach for high dimensional replicability analysis

发布时间：2023-03-29 作者：浏览次数：

Speaker:	曹宏媛	DateTime:	3月30日（周四）10：30-11：30
Brief Introduction to Speaker: Hongyuan Cao is currently an associate professor of statistics at Florida State Univeristy. Her research interests include high dimensional data, large scale multiple testing, survival analysis, and longitudinal data analysis.		Place:	6号楼二楼报告厅
Abstract:Researchers are interested in combining information across multiple (heterogeneous) studies to discover if findings are reproducible in different populations or in different studies. The goal is to increase the power and control the false discovery rate, which results in reliable and robust scientific findings. The focus of our study is to draw inferences regarding a large number of features, such as gene expression, DNA methylation, and others. Rather than combining the underlying raw data, which is not always easy due to differences in the experimental designs, most approaches, including our proposed approach, are based on p-values derived from individual studies. We demonstrate theoretically that the proposed method controls the false discovery rate (FDR). Extensive simulation studies show that the proposed method has higher power than the existing methods while controlling the FDR. Datasets from spatial transcriptomic studies are used to illustrate our methodology.