Meunier Bruno, Bouley Julien, Piec Isabelle, Bernard Carine, Picard Brigitte, Hocquette Jean-François
INRA, Clermont-Ferrand Research Center, Herbivore Research Unit, Muscle Growth and Metabolism Group, 63122 St-Genès-Champanelle, France.
Anal Biochem. 2005 May 15;340(2):226-30. doi: 10.1016/j.ab.2005.02.028.
The recent development of microarray technology has led statisticians and bioinformaticians to develop new statistical methodologies for comparing different biological samples. The objective is to identify a small number of differentially expressed genes from among thousands. In quantitative proteomics, analysis of protein expression using two-dimensional gel electrophoresis shows some similarities with transcriptomic studies. Thus, the goal of this study was to evaluate different data analysis methodologies widely used in array analysis using different proteomic data sets of hundreds of proteins. Even with few replications, the significance analysis of microarrays method appeared to be more powerful than the Student's t test in truly declaring differentially expressed proteins. This procedure will avoid wasting time due to false positives and losing information with false negatives.
微阵列技术的最新发展促使统计学家和生物信息学家开发新的统计方法，用于比较不同的生物样本。目的是从数千个基因中识别出少数差异表达的基因。在定量蛋白质组学中，使用二维凝胶电泳分析蛋白质表达与转录组学研究有一些相似之处。因此，本研究的目的是使用数百种蛋白质的不同蛋白质组数据集，评估广泛用于阵列分析的不同数据分析方法。即使重复次数很少，微阵列显著性分析方法在真正判定差异表达蛋白质方面似乎比学生t检验更有效。这个过程将避免因假阳性而浪费时间，以及因假阴性而丢失信息。
