Biological data sets, such as gene expressions or protein levels, are often high-dimensional, and thus difficult to interpret. Finding important structural features and identifying clusters in an unbiased fashion is a core issue for understanding biological phenomena. In this talk, we describe methods to score periodicity of time series which can be applied to biological processes such as the cell cycle. We consider trending, damping and local vs. global time dependence. Furthermore, we discuss clustering algorithms based on optimal transport to classify time signals based on their overall periodicity.