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

Junyan Duan, Michelle N. Ngo, Satya Swaroop Karri, Lam C. Tsoi, Johann E. Gudjonsson, Babak Shahbaba (), John Lowengrub () and Bogi Andersen ()
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Junyan Duan: University of California Irvine
Michelle N. Ngo: University of California Irvine
Satya Swaroop Karri: University of California Irvine
Lam C. Tsoi: University of Michigan
Johann E. Gudjonsson: University of Michigan
Babak Shahbaba: University of California Irvine
John Lowengrub: University of California Irvine
Bogi Andersen: University of California Irvine

Nature Communications, 2024, vol. 15, issue 1, 1-17

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-48041-6

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