Computational synchronization improves the consistency of gene expression patterns over multiple experiments

Gene expression level is measured on a population of cells in the microarray experiments. Due to the inevitable decay of cell synchrony, the intrinsic gene expression patterns are blurred in the observed time-series microarray data. Furthermore, slight differences in the experimental condition could be amplified as the experiments continue, and eventually considerably influence the cell synchrony, which dominates the way how the intrinsic expression patterns are blurred in the observed microarray data. Therefore, the decay of cell synchrony can cause variability between different experiments. In this paper, we demonstrate how to utilize computational synchronization approach to eliminate the variability caused by cell synchrony between different microarray experiments of Plasmodium falciparum. Specifically, The intrinsic expression patterns are respectively reconstructed for each microarray data set. The preliminary evaluations conducted on synthetic data suggest that computational synchronization could be a useful approach to improve the consistency of gene expression patterns over multiple experiments.