Khan Haseeb Ahmad
Research Centre, Armed Forces Hospital, T-835, Riyadh 11159, Saudi Arabia. khan
Comp Funct Genomics. 2004;5(1):39-47. doi: 10.1002/cfg.369.
The massive surge in the production of microarray data poses a great challenge for proper analysis and interpretation. In recent years numerous computational tools have been developed to extract meaningful interpretation of microarray gene expression data. However, a convenient tool for two-groups comparison of microarray data is still lacking and users have to rely on commercial statistical packages that might be costly and require special skills, in addition to extra time and effort for transferring data from one platform to other. Various statistical methods, including the t-test, analysis of variance, Pearson test and Mann-Whitney U test, have been reported for comparing microarray data, whereas the utilization of the Wilcoxon signed-rank test, which is an appropriate test for two-groups comparison of gene expression data, has largely been neglected in microarray studies. The aim of this investigation was to build an integrated tool, ArraySolver, for colour-coded graphical display and comparison of gene expression data using the Wilcoxon signed-rank test. The results of software validation showed similar outputs with ArraySolver and SPSS for large datasets. Whereas the former program appeared to be more accurate for 25 or fewer pairs (n < or = 25), suggesting its potential application in analysing molecular signatures that usually contain small numbers of genes. The main advantages of ArraySolver are easy data selection, convenient report format, accurate statistics and the familiar Excel platform.
微阵列数据产量的大幅增长给正确的分析和解读带来了巨大挑战。近年来,已开发出众多计算工具来提取微阵列基因表达数据的有意义解读。然而,仍缺乏一个用于微阵列数据两组比较的便捷工具,用户不得不依赖商业统计软件包,这些软件包可能成本高昂且需要特殊技能,此外还需花费额外的时间和精力将数据从一个平台转移到另一个平台。已有多种统计方法,包括t检验、方差分析、皮尔逊检验和曼-惠特尼U检验,被报道用于比较微阵列数据,而威尔科克森符号秩检验作为基因表达数据两组比较的合适检验方法,在微阵列研究中却 largely被忽视。本研究的目的是构建一个集成工具ArraySolver,用于使用威尔科克森符号秩检验对基因表达数据进行颜色编码的图形显示和比较。软件验证结果表明,对于大型数据集,ArraySolver和SPSS的输出相似。而对于25对或更少对的数据(n≤25),前一个程序似乎更准确,这表明它在分析通常包含少量基因的分子特征方面具有潜在应用。ArraySolver的主要优点是数据选择容易、报告格式便捷、统计准确以及基于熟悉的Excel平台。