Single-cell RNA sequencing(scRNA-seq),which permits transcriptional profiling of individual cells,has been applied to heterogeneous groups of cells to study growth and development of tissues and tumors.Resolving cell cycle transcriptional dynamics and assessing cell cycle status for such groups of cells are essential to gain a thorough understanding of the above processes,but may not be adequately achieved by commonly used approaches.Here we developed a computational method to recover cell cycle along time(reCAT)for unsynchronized single-cell transcriptome data.First,reCAT recovers a high-resolution pseudo cell cycle time-series by solving a traveling salesman model.Next,reCAT applies a hidden Markov model(HMM)to segment the time-series into cell cycle stages.reCAT was independently tested for accuracy and reliability using several datasets.