报告题目:Local false discovery rate estimation using feature reliability in LC/MS metabolomics data
报告时间:2016年6月13号上午10:00
报告地点:生医学院电子所楼103会议室
联 系 人：孙啸
摘要: False discovery rate (FDR) control is an important tool of statistical inference in feature selection. In mass spectrometry-based metabolomics data, features can be measured at different levels of reliability and false features are often detected in untargeted metabolite profiling as chemical and/or bioinformatics noise. The traditional false discovery rate methods treat all features equally, which can cause substantial loss of statistical power to detect differentially expressed features. We propose a reliability index for mass spectrometry-based metabolomics data with repeated measurements, which is quantified using a composite measure. We then present a new method to estimate the local false discovery rate (lfdr) that incorporates feature reliability. In simulations, our proposed method achieved better balance between sensitivity and controlling false discovery, as compared to traditional lfdr estimation. We applied our method to a real metabolomics dataset and were able to detect more differentially expressed metabolites that were biologically meaningful.
报告人简介: 于天维1997年本科毕业于清华大学生物系，2000年在清华大学获生物化学硕士学位，2005年获加州大学洛杉矶分校（UCLA）统计学博士学位。从2006年至今，于天维任教于埃墨里大学生物统计与生物信息学系，现为副教授。