Identification of Pseudo-Periodic Gene Expression Profiles

Time-course gene expression profiles associated with periodic biological processes should appear periodic. However, because of inherit problems with the experimental protocols measured gene expression data are actually pseudo-periodic, not exactly periodic. Therefore, identifying pseudo-periodically expressed gene from their time-course data could help understand the molecular mechanism of periodic biological processes. This paper proposes a method for identifying pseudo-periodic gene expression profiles. In the proposed method, a pseudo-periodic gene expression profile is modeled by a linear combination of trigonometric and exponential functions in time plus a Gaussian noise term. A two-step parameter estimation method is employed for estimating parameters in the model. On the other hand, non-pseudo periodic gene expression profiles are model by a constant plus a Gaussian noise term. The statistic F-testing is used to make a decision if a gene is pseudo-periodically expressed or not. Three biological datasets were employed to evaluate the performance of the proposed method. The results show that the proposed method can effectively identify pseudo-periodically expressed genes.