A Hybrid Computational Method for the Identification of Cell Cycle-regulated Genes

Gene expression microarrays are the most commonly available source of high-throughput biological data. They have been widely employed in recent years for the definition of cell cycle regulated (or periodically expressed) subsets of the genome in a number of different organisms. These have driven the development of various computational methods for identifying periodical expressed genes. However, the agreement is remarkably poor when different computational methods are applied to the same data. In view of this, we are motivated to propose herein a hybrid computational method targeting the identification of periodically expressed genes, which is based on a hybrid aggregation of estimations, generated by different computational methods. The proposed hybrid method is benchmarked against three other computational methods for the identification of periodically expressed genes: statistical tests for regulation and periodicity and a combined test for regulation and periodicity. The hybrid method is shown, together with the combined test, to statistically significantly outperform the statistical test for periodicity. However, the hybrid method is also demonstrated to be significantly better than the combined test for regulation and periodicity