https://scholars.lib.ntu.edu.tw/handle/123456789/521782
標題: | Detecting differentially expressed genes in heterogeneous diseases using control-only analysis of variance | 作者: | Tzeng I.-S. WEN-CHUNG LEE |
公開日期: | 2012 | 卷: | 22 | 期: | 8 | 起(迄)頁: | 598-602 | 來源出版物: | Annals of Epidemiology | 摘要: | Purpose: Microarray technology allows for simultaneously screening many genes and determining which gene(s) are differentially expressed in different disease statuses or different cell types. The analysis of variance (ANOVA) (for a K-sample situation with K>2) can be used in such occasions to gauge statistical significances. However, the test may be underpowered if the diseases under study are heterogeneous. Methods: The authors propose the "control-only ANOVA" for detecting differentially expressed genes in heterogeneous diseases. Monte-Carlo simulation shows that the test produces quite accurate type I error rates for both normal and non-normal data. The statistical power of the control-only ANOVA is higher than that of the conventional ANOVA when the diseases under study are heterogeneous. Results: Analysis of a real data set shows that after Bonferroni correction, the control-only ANOVA detects three differentially expressed genes, whereas the conventional ANOVA can detect only one. Conclusions: The control-only ANOVA is recommended for use when the diseases under study are heterogeneous. ? 2012 Elsevier Inc. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84863838900&doi=10.1016%2fj.annepidem.2012.04.017&partnerID=40&md5=294c9f6d38061c29b3723f560e0ed538 https://scholars.lib.ntu.edu.tw/handle/123456789/521782 |
ISSN: | 1047-2797 | DOI: | 10.1016/j.annepidem.2012.04.017 | SDG/關鍵字: | analysis of variance; analytical error; article; controlled study; DNA microarray; gene expression; genetic heterogeneity; genetic screening; human; Monte Carlo method; power analysis; priority journal; sample size; statistical significance; Analysis of Variance; Computer Simulation; Gene Expression Profiling; Genetic Heterogeneity; Genetic Testing; Humans; Models, Genetic; Models, Statistical; Monte Carlo Method |
顯示於: | 流行病學與預防醫學研究所 |
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