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

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.

Universal method for robust detection of circadian state from gene expression.

Circadian clocks play a key role in regulating a vast array of biological processes, with significant implications for human health. Accurate assessment of physiological time using transcriptional biomarkers found in human blood can significantly improve diagnosis of circadian disorders and optimize the delivery time of therapeutic treatments. To be useful, such a test must be accurate, minimally burdensome to the patient, and readily generalizable to new data. A major obstacle in development of gene expression biomarker tests is the diversity of measurement platforms and the inherent variability of the data, often resulting in predictors that perform well in the original datasets but cannot be universally applied to new samples collected in other settings. Here, we introduce TimeSignature, an algorithm that robustly infers circadian time from gene expression. We demonstrate its application in data from three independent studies using distinct microarrays and further validate it against a new set of samples profiled by RNA-sequencing. Our results show that TimeSignature is more accurate and efficient than competing methods, estimating circadian time to within 2 h for the majority of samples. Importantly, we demonstrate that once trained on data from a single study, the resulting predictor can be universally applied to yield highly accurate results in new data from other studies independent of differences in study population, patient protocol, or assay platform without renormalizing the data or retraining. This feature is unique among expression-based predictors and addresses a major challenge in the development of generalizable, clinically useful tests.