报告题目：Mixed model enrichment analysis of gene expression data

报 告 人：Duo Jiang 教授 美国俄勒冈州立大学

报告时间：2019年7月10日上午10:20-11:00

报告地点：数学楼一楼第一报告厅

报告摘要:

Competitive gene-set analysis, also called enrichment analysis, is a widely used tool for interpreting high-throughput biological data such as gene expression data. It aims at testing a known category (e.g. a pathway) of genes for enriched differential expression (DE) signals compared to genes not in the category. Most conventional enrichment testing methods ignore the widespread correlations among genes, which has been shown to result in excessive false positives. We evaluate, both methodologically and empirically, previous methods to account for correlations, and show that they fail to accommodate the DE heterogeneity across genes and can result in severely mis-calibrated type I error and/or power loss. We propose a new framework, MEACA, for gene-set testing based on a mixed effects quasi-likelihood model. Our method flexibly incorporates the unknown distribution of DE effects, and effectively adjusts for completely unknown, unstructured correlations among genes. Compared to existing methods such as GSEA and CAMERA, MEACA enjoys robust and substantially improved control over type I error and maintains good power in a variety of correlation structure and differential expression settings. We also present two real data analyses to illustrate the advantage of our approach.

报告人简介：

Duo Jiang是美国俄勒冈州立大学的助理教授。 她于2014年获得了芝加哥大学的统计学博士学位，在此之前她获得了清华大学的学士学位。她的研究重点是开发遗传学和基因组学数据的统计方法。最近的一些项目涉及微生物组数据分析，基因表达数据的富集分析以及多组学数据整合。