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

Benjamin J. Auerbach (), Garret A. FitzGerald and Mingyao Li ()
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Benjamin J. Auerbach: University of Pennsylvania Perelman School of Medicine
Garret A. FitzGerald: University of Pennsylvania Perelman School of Medicine
Mingyao Li: University of Pennsylvania Perelman School of Medicine

Nature Communications, 2022, vol. 13, issue 1, 1-14

Abstract: 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 simulations and analyses of real data, we demonstrate that Tempo yields more accurate estimates of circadian phase than existing methods and provides well-calibrated uncertainty quantifications. Tempo will facilitate large-scale studies of single-cell circadian transcription.

Date: 2022
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DOI: 10.1038/s41467-022-34185-w

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