Filtering for improved gene selection on microarray data

Many genes and a small number of samples are problematic characteristics of microarray datasets. We investigated the impact on classification accuracy of gene selection approaches on filtered-to-200-gene datasets. Four datasets were used with 3 filters: Student's t-test, information gain, and reliefF. We applied Iterative Feature Perturbation (IFP) and Recursive Feature Elimination (SVM-RFE) for further gene selection. Both approaches resulted in accuracy improvement when used with the t-test-filtered datasets, but not when information gain or reliefF were used. An AUC analysis of the IFP and SVM-RFE accuracy curves indicated that both methods reached the highest AUC values after t-test filtering. A statistical analysis of accuracy across the best 50 genes using the Friedman/Holm test showed that IFP and SVM-RFE were significantly more accurate more often when applied to the t-test-filtered gene sets. Surprisingly, the simple t-test, applied as a filter, results in the best overall SVM accuracy and is at least as accurate as the other, more complicated filter methods.