biostudies-literatureAuerbach BJU.S. Department of Health & Human Services | NIH | National Center for Advancing Translational SciencesU.S. Department of Health & Human Services | NIH | National Institute of General Medical SciencesU.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences (NCATS)U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI)U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute6580https://www.ebi.ac.uk/biostudies/studies/S-EPMC9630322biostudies-literatureUnknown13(1)The circadian clock is a 24 h cellular timekeeping mechanism that regulates human physiology. Answering several fundamental questions in circadian biology will require joint measures of single-cell circadian phases and transcriptomes. However, no widespread experimental approaches exist for this purpose. While computational approaches exist to infer cell phase directly from single-cell RNA-sequencing data, existing methods yield poor circadian phase estimates, and do not quantify estimation uncertainty, which is essential for interpretation of results from very sparse single-cell RNA-sequencing data. To address these unmet needs, we introduce Tempo, a Bayesian variational inference approach that incorporates domain knowledge of the clock and quantifies phase estimation uncertainty. ThroughNature communicationsTempo: an unsupervised Bayesian algorithm for circadian phase inference in single-cell transcriptomics.PMC9630322R01GM1253012U54TR0018782T32HG000046-21Auerbach BJFitzGerald GALi MfalseTempo: an unsupervised Bayesian algorithm for circadian phase inference in single-cell transcriptomics.The circadian clock is a 24 h cellular timekeeping mechanism that regulates human physiology. Answering several fundamental questions in circadian biology will require joint measures of single-cell circadian phases and transcriptomes. However, no widespread experimental approaches exist for this purpose. While computational approaches exist to infer cell phase directly from single-cell RNA-sequencing data, existing methods yield poor circadian phase estimates, and do not quantify estimation uncertainty, which is essential for interpretation of results from very sparse single-cell RNA-sequencing data. To address these unmet needs, we introduce Tempo, a Bayesian variational inference approach that incorporates domain knowledge of the clock and quantifies phase estimation uncertainty. Through2022-01-01T00:00:00Z2022 Nov2025-04-05T11:56:57.459Z2025-02-19T00:31:32.888ZS-EPMC96303223632366810.1038/s41467-022-34185-w