Tempo: an unsupervised Bayesian algorithm for circadian phase inference in single-cell transcriptomics
Year: 2022
Type: article
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.
Source: Nature Communications
Institution: University of Pennsylvania
Language: English
Subfield: Endocrine and Autonomic Systems
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,890
OpenAlex ID: W4308147678