A rank sum test method for informative gene discovery

Finding informative genes from microarray data is an important research problem in bioinformatics research and applications. Most of the existing methods rank features according to their discriminative capability and then find a subset of discriminative genes (usually top k genes). In particular, t-statistic criterion and its variants have been adopted extensively. This kind of methods rely on the statistics principle of t-test, which requires that the data follows a normal distribution. However, according to our investigation, the normality condition often cannot be met in real data sets. To avoid the assumption of the normality condition, in this paper, we propose a rank sum test method for informative gene discovery. The method uses a rank-sum statistic as the ranking criterion. Moreover, we propose using the significance level threshold, instead of the number of informative genes, as the parameter. The significance level threshold as a parameter carries the quality specification i...

• Type: Conference
• Year: 2004
• Where: KDD
• Authors: Lin Deng, Jian Pei, Jinwen Ma, Dik Lun Lee
• Keywords: Data Mining, KDD 2004, Rank Sum Method, Rank Sum Test, Significance Level Threshold