Decoding Cell-level Circadian Phase and Rhythm-state Remodeling in Single-cell Transcriptomes With scRhythmBayes

Abstract

Current circadian omics analysis tools have been developed primarily for bulk time-series. However, heterogeneous single-cell datasets require analysis and interpretation of rhythmic programs across different cells, cell types, and biological conditions. In these datasets, cellular circadian phase is usually unobserved, and sample-level zeitgeber time provides temporal information but does not define the exact phase of every cell. Existing methods address only selected aspects of this problem and do not model how cell-level phase evidence informs gene-level rhythmic parameters and rhythm-state remodeling. Here we present scRhythmBayes, a new probabilistic algorithm for phase-resolved circadian analysis in heterogeneous single-cell transcriptomes. scRhythmBayes combines a seed-gene count likelihood with a stratum-aware circular prior to infer cell-level circadian phase, incorporates phase support into gene-level rhythmic modeling, and assigns posterior probabilities to interpretable rhythm-state categories, including rhythm gain, rhythm loss, amplitude remodeling and phase shifts. In heterogeneous simulations, scRhythmBayes achieved higher phase-recovery accuracy than existing phase-inference methods and maintained balanced performance across rhythmic gene detection, parameter recovery, and rhythm-state classification. Stress tests defined practical requirements for seed-gene support, zeitgeber anchoring, temporal sampling density, and cell number. In mouse liver, scRhythmBayes revealed distinct circadian phase occupancy and rhythm-state organization between periportal and pericentral hepatocytes, supporting a temporal dimension of liver zonation. It also identified cell-type-specific rhythmic markers that were largely distinct from conventional static markers, indicating that cellular identity includes temporal programs not captured by average expression. In human heart disease data, phase-coordination and phase-distribution analyses identified cell-type-selective temporal disorganization rather than uniform circadian disruption across the tissue. scRhythmBayes therefore provides a statistically grounded approach for resolving circadian organization across cells, genes, cell types and biological conditions.