maSigPro:: a method to identify significantly differential expression profiles in time-course microarray experiments
maSigPro:: a method to identify significantly differential expression profiles in time-course microarray experiments
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DOI：
10.1093/bioinformatics/btl056
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发表时间：
2006-05-01
期刊：
影响因子：
5.8
通讯作者：
Talón, M
中科院分区：
文献类型：
--
作者：
Conesa, A;Nueda, MJ;Talón, M
动机：多系列时程微阵列实验是探索生物过程的有用方法。在此类实验中，研究人员经常对研究基因表达随时间的变化以及评估不同实验组之间的趋势差异感兴趣。大量的数据、实验条件的多样性和实验的动态性质给数据分析带来了巨大的挑战。结果：在这项工作中，我们提出了一种统计程序来识别在时间过程实验中跨分析组显示不同基因表达谱的基因。该方法是一种两步回归方法，其中实验组由虚拟变量来识别。该程序首先使用所有定义的变量调整全局回归模型，以识别差异表达的基因，然后应用变量选择策略来研究组之间的差异并找到统计上显着的不同概况。该方法在真实和模拟的微阵列数据集上进行了说明。
Motivation: Multi-series time-course microarray experiments are useful approaches for exploring biological processes. In this type of experiments, the researcher is frequently interested in studying gene expression changes along time and in evaluating trend differences between the various experimental groups. The large amount of data, multiplicity of experimental conditions and the dynamic nature of the experiments poses great challenges to data analysis.Results: In this work, we propose a statistical procedure to identify genes that show different gene expression profiles across analytical groups in time-course experiments. The method is a two-regression step approach where the experimental groups are identified by dummy variables. The procedure first adjusts a global regression model with all the defined variables to identify differentially expressed genes, and in second a variable selection strategy is applied to study differences between groups and to find statistically significant different profiles. The methodology is illustrated on both a real and a simulated microarray dataset.