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tauFisher predicts circadian time from a single sample of bulk and single-cell pseudobulk transcriptomic data

Author

Listed:
  • Junyan Duan

    (University of California Irvine
    University of California Irvine)

  • Michelle N. Ngo

    (University of California Irvine
    University of California Irvine)

  • Satya Swaroop Karri

    (University of California Irvine)

  • Lam C. Tsoi

    (University of Michigan
    University of Michigan
    University of Michigan
    University of Michigan)

  • Johann E. Gudjonsson

    (University of Michigan
    University of Michigan)

  • Babak Shahbaba

    (University of California Irvine
    University of California Irvine)

  • John Lowengrub

    (University of California Irvine
    University of California
    University of California Irvine)

  • Bogi Andersen

    (University of California Irvine
    University of California Irvine
    University of California Irvine)

Abstract

As the circadian clock regulates fundamental biological processes, disrupted clocks are often observed in patients and diseased tissues. Determining the circadian time of the patient or the tissue of focus is essential in circadian medicine and research. Here we present tauFisher, a computational pipeline that accurately predicts circadian time from a single transcriptomic sample by finding correlations between rhythmic genes within the sample. We demonstrate tauFisher’s performance in adding timestamps to both bulk and single-cell transcriptomic samples collected from multiple tissue types and experimental settings. Application of tauFisher at a cell-type level in a single-cell RNAseq dataset collected from mouse dermal skin implies that greater circadian phase heterogeneity may explain the dampened rhythm of collective core clock gene expression in dermal immune cells compared to dermal fibroblasts. Given its robustness and generalizability across assay platforms, experimental setups, and tissue types, as well as its potential application in single-cell RNAseq data analysis, tauFisher is a promising tool that facilitates circadian medicine and research.

Suggested Citation

  • Junyan Duan & Michelle N. Ngo & Satya Swaroop Karri & Lam C. Tsoi & Johann E. Gudjonsson & Babak Shahbaba & John Lowengrub & Bogi Andersen, 2024. "tauFisher predicts circadian time from a single sample of bulk and single-cell pseudobulk transcriptomic data," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-48041-6
    DOI: 10.1038/s41467-024-48041-6
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