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BIMSA Lecture
High-dimensional IV regression for genetical genomics data incorporating network structures
High-dimensional IV regression for genetical genomics data incorporating network structures
组织者
演讲者
崔跃华
时间
2023年06月26日 11:00 至 12:00
地点
JCY-3
线上
Zoom 271 534 5558
(YMSC)
摘要
Genetical genomics data present promising opportunities for integrating gene expression and genotype information. Lin et al. (2015) proposed an instrumental variables (IV) regression framework to select important genes with high-dimensional genetical genomics data. The IV regression addresses the issue of endogeneity caused by potential correlations between gene expressions and error terms, thereby improving gene selection performance. Knowing that genes function in networks to fulfill their joint task, incorporating network structures into a regression model can further enhance gene selection performance. In this presentation, I will introduce a graph-constrained penalized IV regression framework for high-dimensional genetical genomic data, aiming to improve gene selection performance by incorporating gene network structures. We propose a two-step estimation procedure that adopts a network-constrained regularization method and establishes selection consistency. Furthermore, considering that gene expressions are time-dependent, we extend the framework to allow for the effect of gene expressions to vary over time within a varying-coefficients IV regression framework. We demonstrate the utility of our method through simulations and real data analysis.
This is a joint work with Bin Gao, Jialin Qu, Xu Liu and Hongzhe Li.
演讲者介绍
崔跃华现任密西根州立大学（MSU）统计与概率系教授，曾任研究生部主任。他的研究方向主要集中在统计遗传学与基因组学，重点关注基因–基因互作、基因–环境互作、多组学数据整合、因果推断以及空间转录组等领域。他在 Nature Communications、Nucleic Acids Research、Advanced Science和 Biometrics 等国际知名期刊发表了百余篇学术论文。崔教授为美国统计学会（ASA）会士及国际统计学会（ISI）当选会员。他目前担任 PLOS Genetics 和 PLOS Computational Biology 的学术编辑，同时担任包括 Statistics and Probability Letters 和 Statistical Applications in Genetics and Molecular Biology 在内的多本统计学与计算基因组学期刊的副主编。