报告主题：Biomarker-guided heterogeneity analysis of genetic regulations via multivariate sparse fusion
报告摘要：Heterogeneity is a hallmark of many complex diseases. There are multiple ways of defining heterogeneity, among which the heterogeneity in genetic regulations, for example GEs (gene expressions) by CNVs (copy number variations) and methylation, has been suggested but little investigated. The heterogeneity in genetic regulations can be linked with disease severity, progression, and other traits and is biologically highly important. However, the analysis can be very challenging with the high dimensionality of both sides of regulation and sparse and weak signals. In this article, we consider the scenario where subjects form unknown subgroups, and each subgroup has unique genetic regulation relationships. Further, such heterogeneity is “guided" by a known biomarker. We develop an MSF (Multivariate Sparse Fusion) approach, which innovatively applies the penalized fusion technique to simultaneously determine the number and structure of subgroups and regulation relationships within each subgroup. An effective computational algorithm is developed, and extensive simulations are conducted. The analysis of heterogeneity in the GE-CNV regulations in melanoma and GE-methylation regulations in stomach cancer using the TCGA (The Cancer Genome Atlas) data leads to interesting findings.
个人简介：孙怡帆，中国人民大学统计学院副教授，博士生导师。数理统计系系主任，全国工业统计学教学研究会第九届理事会理事。主要研究方向为复杂数据分析、网络分析、最优化方法等，在Statistics in Medicine，统计研究等学术期刊发表论文20余篇，主持国家和省部级等项目6项。
主持人简介：李扬，中国人民大学统计学院教授，统计咨询研究中心主任，国际统计学会推选会员、国际生物统计学会中国分会青年理事、北京生物医学统计与数据管理研究会监事长。主要从事相关型数据分析, 模型选择与不确定性评价, 潜变量建模，临床试验设计等领域研究。