Functional gene clustering is a statistical approach for identifying the temporal patterns of gene expression measured at a series of time points. I will discuss two approaches for functional clustering, one designed to estimate periodic transcriptional profiles using Fourier series, and the second relying on wavelets for dimension reduction. The covariance matrix of the serial measurements over time for each gene is flexibly modeled through an autoregressive moving-average process of order (p, q). An EM algorithm is used to estimate the unknown parameters, and the model is chosen through a selection criterion. The methods are shown to be effective in simulation studies and on recent yeast data.