TimeCycle is designed to detect rhythmic genes in circadian transcriptomic time-series data. Based on topological data analysis, TimeCycle provides a reliable and efficient reference-free framework for cycle detection — handling custom sampling schemes,...
TimeTrial is a user-friendly benchmarking framework using both real and synthetic data to investigate cycle detection algorithms’ performance and improve circadian experimental design. While algorithms for detecting cycling transcripts have advanced, there remains...
TimeSignature is a machine-learning approach to infer physiological time based on gene expression in human blood. A powerful feature is TimeSignature’s generalizability, enabling it to be applied to samples from disparate studies and yield highly accurate results...
