Tempo: an unsupervised Bayesian algorithm for circadian phase inference in single-cell transcriptomics.

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

SUBMITTER: Auerbach BJ 

PROVIDER: S-EPMC9630322 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature