An R package suite for microarray meta-analysis in quality control, differentially expressed gene analysis and pathway enrichment detection

作     者：Wang, Xingbin Kang, Dongwan D. Shen, Kui Song, Chi Lu, Shuya Chang, Lun-Ching Liao, Serena G. Huo, Zhiguang Tang, Shaowu Ding, Ying Kaminski, Naftali Sibille, Etienne Lin, Yan Li, Jia Tseng, George C. 

作者机构：Henry Ford Hlth Syst Detroit MI 48202 USA Univ Pittsburgh Dept Human Genet Pittsburgh PA 15261 USA Univ Calif Berkeley Lawrence Berkeley Natl Lab Genom Div Berkeley CA 94720 USA Univ Pittsburgh Magee Womens Res Inst Pittsburgh PA USA Univ Pittsburgh Dept Biostat Pittsburgh PA 15261 USA PharmaNet I3 Hunt Valley MD 21031 USA Univ Pittsburgh Dept Computat & Syst Biol Pittsburgh PA USA Univ Pittsburgh Div Pulm Allergy & Crit Care Med Pittsburgh PA USA Univ Pittsburgh Dept Psychiat Pittsburgh PA USA 

出 版 物：《BIOINFORMATICS》 (生物信息学)

年 卷 期：2012年第28卷第19期

页      面：2534-2536页

核心收录：

学科分类：0710[理学-生物学] 08[工学] 0714[理学-统计学（可授理学、经济学学位）] 0836[工学-生物工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Institutes of Health [MH077159  MH094862  HL095397  HL101715] 

主　　题：MICROARRAY technology META-analysis GENE expression QUALITY control GENOMICS BIOINFORMATICS 

摘      要：With the rapid advances and prevalence of high-throughput genomic technologies, integrating information of multiple relevant genomic studies has brought new challenges. Microarray meta-analysis has become a frequently used tool in biomedical research. Little effort, however, has been made to develop a systematic pipeline and user-friendly software. In this article, we present MetaOmics, a suite of three R packages MetaQC, MetaDE and MetaPath, for quality control, differentially expressed gene identification and enriched pathway detection for microarray meta-analysis. MetaQC provides a quantitative and objective tool to assist study inclusion/exclusion criteria for meta-analysis. MetaDE and MetaPath were developed for candidate marker and pathway detection, which provide choices of marker detection, meta-analysis and pathway analysis methods. The system allows flexible input of experimental data, clinical outcome (case-control, multi-class, continuous or survival) and pathway databases. It allows missing values in experimental data and utilizes multi-core parallel computing for fast implementation. It generates informative summary output and visualization plots, operates on different operation systems and can be expanded to include new algorithms or combine different types of genomic data. This software suite provides a comprehensive tool to conveniently implement and compare various genomic meta-analysis pipelines.