Yee Hwa Yang1, Yuanyuan Xiao, Mark R Segal. 1. Departments of Medicine, Center for Bioinformatics and Molecular Biostatistics, University of California San Francisco, CA 94143, USA.
Abstract
MOTIVATION: A common objective of microarray experiments is the detection of differential gene expression between samples obtained under different conditions. The task of identifying differentially expressed genes consists of two aspects: ranking and selection. Numerous statistics have been proposed to rank genes in order of evidence for differential expression. However, no one statistic is universally optimal and there is seldom any basis or guidance that can direct toward a particular statistic of choice. RESULTS: Our new approach, which addresses both ranking and selection of differentially expressed genes, integrates differing statistics via a distance synthesis scheme. Using a set of (Affymetrix) spike-in datasets, in which differentially expressed genes are known, we demonstrate that our method compares favorably with the best individual statistics, while achieving robustness properties lacked by the individual statistics. We further evaluate performance on one other microarray study.
MOTIVATION: A common objective of microarray experiments is the detection of differential gene expression between samples obtained under different conditions. The task of identifying differentially expressed genes consists of two aspects: ranking and selection. Numerous statistics have been proposed to rank genes in order of evidence for differential expression. However, no one statistic is universally optimal and there is seldom any basis or guidance that can direct toward a particular statistic of choice. RESULTS: Our new approach, which addresses both ranking and selection of differentially expressed genes, integrates differing statistics via a distance synthesis scheme. Using a set of (Affymetrix) spike-in datasets, in which differentially expressed genes are known, we demonstrate that our method compares favorably with the best individual statistics, while achieving robustness properties lacked by the individual statistics. We further evaluate performance on one other microarray study.
Authors: Yuki Ohi; Han Qin; Chibo Hong; Laure Blouin; Jose M Polo; Tingxia Guo; Zhongxia Qi; Sara L Downey; Philip D Manos; Derrick J Rossi; Jingwei Yu; Matthias Hebrok; Konrad Hochedlinger; Joseph F Costello; Jun S Song; Miguel Ramalho-Santos Journal: Nat Cell Biol Date: 2011-04-17 Impact factor: 28.824
Authors: Matthew D Burstein; Anna Tsimelzon; Graham M Poage; Kyle R Covington; Alejandro Contreras; Suzanne A W Fuqua; Michelle I Savage; C Kent Osborne; Susan G Hilsenbeck; Jenny C Chang; Gordon B Mills; Ching C Lau; Powel H Brown Journal: Clin Cancer Res Date: 2014-09-10 Impact factor: 12.531