Tracking the herd: Resynchronization analysis of cell-cycle gene expression data in saccharomyces cerevisiae

Identification of genes expressed in a cell-cycle-specific periodical manner is of great interest to understand cyclic systems which play a critical role in many biological processes. However, identification of cell-cycle regulated genes by microarray gene expression data is complicated by the factor of synchronization loss, thus remains a challenging problem. Decomposing the expression measurements will allow to better represent the single-cell behavior and improve the accuracy in identifying periodically expressed genes. In this paper, we propose a resynchronization-based algorithm for identifying cell-cycle-related genes, where we present a simple synchronization loss model by modeling the gene expression measurements as a superposition of different cell populations growing at different rates. The underlying expression profile is reconstructed through resynchronization and is further fitted to the measurements in order to identify periodically expressed genes. Results from both simulations and real mircorarray data show that the proposed scheme is promising for identifying cycling genes and revealing underlying gene expression profiles.