National Institute of Biology, Department of Biotechnology and Systems Biology, Ljubljana, Slovenia.
OMICS. 2010 Apr;14(2):177-86. doi: 10.1089/omi.2009.0126.
This article presents an approach to microarray data analysis using discretised expression values in combination with a methodology of closed item set mining for class labeled data (RelSets). A statistical 2 x 2 factorial design analysis was run in parallel. The approach was validated on two independent sets of two-color microarray experiments using potato plants. Our results demonstrate that the two different analytical procedures, applied on the same data, are adequate for solving two different biological questions being asked. Statistical analysis is appropriate if an overview of the consequences of treatments and their interaction terms on the studied system is needed. If, on the other hand, a list of genes whose expression (upregulation or downregulation) differentiates between classes of data is required, the use of the RelSets algorithm is preferred. The used algorithms are freely available upon request to the authors.
本文提出了一种使用离散表达值结合闭项集挖掘方法(RelSets)对微阵列数据分析的方法,用于标记类别的数据。同时进行了统计 2x2 析因设计分析。该方法在使用马铃薯植物的两个双色微阵列实验的两个独立数据集上进行了验证。我们的结果表明,应用于相同数据的两种不同分析程序足以解决两个不同的生物学问题。如果需要对处理及其相互作用项对所研究系统的影响进行概述,则需要进行统计分析。另一方面,如果需要一组基因的表达(上调或下调)来区分数据类,则首选使用 RelSets 算法。所使用的算法可根据作者的要求免费提供。