A novel filter feature selection method for paired microarray expression data analysis.

Abstract

In recent years, a large amount of microarray data sets are produced with tens of thousands of genes. Feature selection has become a very sharp tool to select the informative genes. However, few feature selection methods consider the effect of paired samples, which are much more considered in the experiments of these years. Here, we propose a new feature selection method for paired microarray data sets analysis. It uses the fold change instead of the subtraction in the original approach, measures the statistical significant using the q-value of False Discovery Rate (FDR) and also decreases the influence of redundant genes. We compare the proposed method with another six existing methods in predict performance, stability of gene lists, functional stability and functional enrichment analysis using six kinds of paired cancer data sets. Comparison results show that our proposed method achieves better effectiveness, stability and consistency when it is applied to paired data sets.