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Author Spotlight: In Vitro Investigations of Circadian Rhythms in Multicellular Systems

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An improved rhythmicity analysis method using Gaussian Processes detects cell-density dependent circadian

Shabnam Sahay^1,2, Shishir Adhikari^3,4, Sahand Hormoz^3,4,5

¹Department of Computer Science, Indian Institute of Technology Bombay.

Biorxiv : the Preprint Server for Biology

|March 30, 2023

Summary

We introduce Oscillation Detection using Gaussian Processes (ODeGP), a new method for detecting rhythmic patterns in biological time-series data. ODeGP successfully identifies weak and non-stationary oscillations, even discovering unexpected gene rhythms in stem cells.

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Area of Science:

Chronobiology
Systems Biology
Computational Biology

Background:

Detecting biological rhythms in time-series data is challenging due to low amplitude, high variability, and non-stationarity.
Existing methods often fail to adequately handle the complexities of biological rhythm detection.

Approach:

We developed Oscillation Detection using Gaussian Processes (ODeGP), integrating Gaussian Process regression and Bayesian inference.
ODeGP incorporates measurement errors, non-uniform sampling, and a novel kernel for non-stationary waveforms.
The method utilizes Bayes factors for hypothesis testing, modeling both rhythmic and non-rhythmic states.

Key Points:

ODeGP outperforms eight common methods in detecting stationary and non-stationary oscillations on synthetic datasets.
The method demonstrates superior sensitivity in detecting weak, noisy oscillations in qPCR datasets.
ODeGP revealed unexpected oscillations in the Bmal1 gene in mouse embryonic stem cells influenced by cell density.

Conclusions:

ODeGP provides a flexible and robust approach for detecting oscillations in challenging biological time-series data.
The method can uncover previously undetected rhythmic patterns, offering new insights into biological processes.
ODeGP is currently implemented as an R package for analyzing single or few time-trajectories.