{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Auerbach BJ"],"funding":["U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences","U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences","U.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 Institute"],"pagination":["6580"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9630322"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["13(1)"],"pubmed_abstract":["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. Through"],"journal":["Nature communications"],"pubmed_title":["Tempo: an unsupervised Bayesian algorithm for circadian phase inference in single-cell transcriptomics."],"pmcid":["PMC9630322"],"funding_grant_id":["R01GM125301","2U54TR001878","2T32HG000046-21"],"pubmed_authors":["Auerbach BJ","FitzGerald GA","Li M"],"additional_accession":[]},"is_claimable":false,"name":"Tempo: an unsupervised Bayesian algorithm for circadian phase inference in single-cell transcriptomics.","description":"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. Through","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Nov","modification":"2025-04-05T11:56:57.459Z","creation":"2025-02-19T00:31:32.888Z"},"accession":"S-EPMC9630322","cross_references":{"pubmed":["36323668"],"doi":["10.1038/s41467-022-34185-w"]}}