Inferring circadian phases and quantifying biological desynchrony across single-cell transcriptomes

Single-cell RNA sequencing (scRNA-seq) reveals heterogeneity in circadian clock states across individual cells, yet accurately inferring circadian phase and distinguishing biological desynchrony from technical noise remains challenging. Here, we introduce scRitmo, a probabilistic framework that infers single-cell circadian phases from mRNA count data, providing both a point estimate and a posterior uncertainty for each cell. A simulation-calibrated variance decomposition separates the observed phase dispersion into biological and technical components, enabling direct estimation of intercellular desynchrony. We validate scRitmo using deeply sequenced unsynchronized fibroblasts, where inferred transcriptomic phases accurately predict protein-level oscillations of a circadian reporter. Applied to murine scRNA-seq datasets from liver, aorta, and skin, scRitmo outperforms existing methods and reveals cell-type-specific levels of phase coherence. In SABER-FISH time-series data, the method recovers the progressive accumulation of desynchrony following synchronization, and in Drosophila clock neurons it captures cell-type-specific phase shifts and the expected increase in phase dispersion under constant darkness relative to light-dark entrainment. Together, scRitmo provides a principled approach for quantifying circadian (de)synchrony from transcriptomic data, decoupling biological phase variability from measurement noise across tissues, organisms, and experimental conditions.