ArticleNature communications2022
Tempo: an unsupervised Bayesian algorithm for circadian phase inference in single-cell transcriptomics.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Chrono-atlas of cell-type specific daily gene expression rhythms in the regenerating colon.Nature communications · 2026Article
- D-Box Binding Protein (DBP) as a Circadian Output Regulator: Molecular Mechanisms, Tissue-Specific Functions, and Disease Relevance.International journal of molecular sciences · 2026Review
- Transcriptional noise sets fundamental limits to decoding circadian clock phase from single-cell RNA snapshots.iScience · 2026Article
- BAYESIAN RHYTHMIC MODEL FOR JOINTLY DETECTING CIRCADIAN BIOMARKERS AND PREDICTING MOLECULAR CIRCADIAN TIME IN HUMAN POST-MORTEM BRAIN TRANSCRIPTOME.bioRxiv : the preprint server for biology · 2026Article
- VIST: variational inference for single cell time series.Genome biology · 2026Article
- DCPR: a deep learning framework for circadian phase reconstruction.BMC bioinformatics · 2025Article
- Variational inference of single cell time series.bioRxiv : the preprint server for biology · 2025Article
- Rhythm profiling using COFE reveals multi-omic circadian rhythms in human cancers in vivo.PLoS biology · 2025Article
- Biases in machine-learning models of human single-cell data.Nature cell biology · 2025Review
- Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations.Nature methods · 2024Article
- Detecting Rhythmic Gene Expression in Single-cell Transcriptomics.Journal of biological rhythms · 2024Article
- Gene expression clock: an unsupervised deep learning approach for predicting circadian rhythmicity from whole genome expression.Neural computing & applications · 2024Article
- Circadian rhythms in cardiovascular (dys)function: approaches for future therapeutics.NPJ cardiovascular health · 2024Review
- Detecting Rhythmic Gene Expression in Single Cell Transcriptomics.bioRxiv : the preprint server for biology · 2024Article
- tauFisher predicts circadian time from a single sample of bulk and single-cell pseudobulk transcriptomic data.Nature communications · 2024Article
- PENN: Phase Estimation Neural Network on Gene Expression Data.The 4th Joint International Conference on Deep Learning, Big Data and Blockchain (DBB 2023). Joint International Conference on Deep Learning, Big Data and Blockchain (4th : 2023 : Marrakech, Morocco ; Online) · 2023Article
- Circadian regulation of pulmonary disease: the importance of timing.Clinical science (London, England : 1979) · 2023Review
- Tempo: an unsupervised Bayesian algorithm for circadian phase inference in single-cell transcriptomics.Nature communications · 2022Article
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3 authors.
Funding
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.