提出集成分析来自相同研究问题的不同数据集来识别表达不稳定的基因.把这一问题形式化为一个非线性整数规划问题,三个启发式的算法被提出来求解这一优化问题;进一步地设计了一个统计量来度量基因的不稳定表达程度.提出的方法应用于两个真实数据,实验结果显示:所识别的不稳定基因在两个数据中的表达不一致;利用表达不稳定基因可以提高差异表达基因的筛选结果,而去除表达不稳定基因可以有效地提高微阵列数据分类.实验结果表明,提出的方法是有效的,并且表达不稳定基因可以为微阵列数据分析提供有价值的信息.

An idea of identifying unstable genes by integrative analysis of pair of different data has been proposed. This problem is modelled as a nonlinear integer programming problem. Three approximate methods have been proposed to work out the solution. An index is designed to measure and rank the instability magnitude of unstable gene. The experimental results on two lung cancer datasets from two research groups demonstrate the identified unstable genes have really unstable expression. The identified unstable genes can be used to improve the result of identifying differential expression genes and excluding these genes can effectively enhance the accuracy of microarray data classification. The findings suggest the proposed methods are effective and the unstable genes can provide valuable information for microarray data analysis.

Supported by the National Natural Science Foundation of China under Grant No.60903086 (国家自然科学基金); the Zhejiang Provincial Natural Science Foundation of China under Grant No.Y1080973 (浙江省自然科学基金)