Abstract: The objective of this project is to evaluate and use a variety of statistical analysis and machine-learning methods (including correlation and clustering analysis, information theory and reservoir computing strategies) to provide a time-line to existing single-cell transcriptomics data sets (which are usually time-less). We will use to that end known circadian genes as anchors, and will attempt to identify other potential contributors to circadian rhythmicity, and potentially uncover previously unknown effects of these rhythms to the functioning of cells. We will employ in particular data from the Cancer Genome Atlas, which will allow us to compare healthy and cancer cells and thereby analyze the potential role of circadian rhythmicity alterations in that disease.