Data shuffling and statistical analysis on microarray data for gene selection: a comparative study on filtering methods

Computational analysis have been broadly used to discover disease-relevant genes from microarray expression data. In this paper, we extend a traditional statistical metric to a second level to measure gene-disease relations, testing such relation whether can be replicated by randomly shuffling the gene expression data. The traditional metric can be considered as a first-level metric; the relevance of each gene is then verified through the second-level significance testing based on the first-level metric calculated on the original data and shuffled data. We show that this method can also produce high classification performance, compared with other filter-based methods.