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. 2024 Jan 16;121(3):e2308114120.
doi: 10.1073/pnas.2308114120. Epub 2024 Jan 8.

Platform-independent estimation of human physiological time from single blood samples

Yitong Huang  1   2 , Rosemary Braun  1   2   3   4   5   6
Affiliations

Platform-independent estimation of human physiological time from single blood samples

Yitong Huang et al. Proc Natl Acad Sci U S A. .

Abstract

Abundant epidemiological evidence links circadian rhythms to human health, from heart disease to neurodegeneration. Accurate determination of an individual's circadian phase is critical for precision diagnostics and personalized timing of therapeutic interventions. To date, however, we still lack an assay for physiological time that is accurate, minimally burdensome to the patient, and readily generalizable to new data. Here, we present TimeMachine, an algorithm to predict the human circadian phase using gene expression in peripheral blood mononuclear cells from a single blood draw. Once trained on data from a single study, we validated the trained predictor against four independent datasets with distinct experimental protocols and assay platforms, demonstrating that it can be applied generalizably. Importantly, TimeMachine predicted circadian time with a median absolute error ranging from 1.65 to 2.7 h, regardless of systematic differences in experimental protocol and assay platform, without renormalizing the data or retraining the predictor. This feature enables it to be flexibly applied to both new samples and existing data without limitations on the transcriptomic profiling technology (microarray, RNAseq). We benchmark TimeMachine against competing approaches and identify the algorithmic features that contribute to its performance.

Keywords: circadian rhythms; cross-platform prediction; machine learning; transcriptomics.

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Conflict of interest statement

Competing interests statement:The authors declare no competing interest.

Figures

Fig. 1.
Fig. 1.
TimeMachine predictions of melatonin phase (time since DLMO) on data from three distinct studies. Both variants of the TimeMachine algorithm, ratio TimeMachine (rTM) and Z-score TimeMachine (zTM), were trained on a subset of subjects from the Möller et al. study (TrTe) and then applied to the remaining test subjects in Möller et al. along with two independent datasets V1 (Archer et al.) and V3 (RNA-Seq) for validation. The top row shows the agreement of predictions from ratio TimeMachine with the measured melatonin phase (time since DLMO) for each sample. Dark and light gray bands indicate an error range of ±2 and ±4 h. The color of the point represents experimental protocols: Black denotes control condition, and red denotes sleep restriction (Möller et al.) and forced desynchrony (Archer et al.), respectively. In the bottom row, we plot the fraction of correctly predicted samples for each study vs. prediction errors for the ratio TimeMachine algorithm (solid black), in comparison to the other variant, Z-score TimeMachine algorithm (dashed purple), and the single-sample PLSR algorithm (dashed green), along with the normalized area under the curves (AUC) and median absolute errors for each algorithm.
Fig. 2.
Fig. 2.
Accuracy of one-timepoint methods on predicting the blood draw-time. Instead of predicting the melatonin phase as in Fig. 1, we trained and applied ratio TimeMachine (solid black), Z-score TimeMachine (dashed purple), and PLSR (dashed green) to obtain time-of-day predictions on four distinct datasets.
Fig. 3.
Fig. 3.
Relationship between prediction accuracy and its predicted amplitude for ratio TimeMachine and Z-score TimeMachine. The amplitude here is defined as the magnitude of the predictor Y^. Predicted amplitudes ≤0.5 yield a significantly less accurate time prediction than higher amplitudes for both variants of TimeMachine (P<0.001; Wilcoxon rank-sum test).
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