摘要
生命科学已进入以功能基因组研究为主的后基因组时代,基因微阵列技术是功能基因组学研究领域最常用的手段,可同时获得大量基因的表达谱数据资料。发现差异表达基因是微阵列实验研究的主要目的之一,本文针对微阵列实验中差异表达基因常用统计分析方法进行综述。多序列两样本比较时,t检验法是最简单的检验差异表达基因的统计分析方法。多序列多组比较时,统计推断可采用方差分析,其中混合效应方差分析(方差分量模型)是含有多个误差来源的多因素微阵列实验有效统计分析方法。其他线性和非线性混合效应模型用于基因表达微阵列数据的统计学分析有待进一步研究。
Life sciences have entered a functional genomics research-based post-genomic era. Microarray technology is the most commonly used method in the functional genomics researches and produces a large number of gene expression profile data. To discover differentially expressed genes is one of the main purposes of mlcroarray experimental studies. This paper reviewed the common statistical tests for differential expressed gene in microarray experiments. The simplest statistical method for detecting differential expression is the t test, which can be used to compare two conditions when there is replication of samples. With more than two conditions, analysis of variance （ANOVA） can be used. and the mixed ANOVA model （variance component model） is a general and powerful approach for microarray experiments with multiple factors and several sources of variation. Other linear and nonlinear mixed effects models for gene expression microarray data analysis need further study.
出处
《现代预防医学》
CAS
北大核心
2008年第4期637-639,645,共4页
Modern Preventive Medicine
基金
国家自然科学基金资助项目(39900126)
陕西省自然科学基金资助项目(2003F11)
关键词
微阵列
差异表达基因
统计分析
Microarray, Differential expressed gene
Statistical analysis
