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Identifying stochastic oscillations in single-cell live imaging time series using Gaussian processes.

Nick E Phillips¹, Cerys Manning¹, Nancy Papalopulu¹

¹Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom.

Plos Computational Biology

|May 12, 2017

Summary

This study introduces a new statistical method to identify noisy oscillatory gene expression in single cells. The approach accurately distinguishes true oscillations from random fluctuations, improving analysis of dynamic biological data.

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Area of Science:

Systems Biology
Single-Cell Analysis
Computational Biology

Background:

Gene expression exhibits complex dynamics, including oscillations, crucial for biological processes.
Single-cell live imaging generates vast dynamic data, but inherent molecular noise complicates analysis.
Distinguishing true oscillatory gene expression from random fluctuations in noisy single-cell data is a significant challenge.

Purpose of the Study:

To develop an objective statistical method for classifying noisy single-cell time series as periodic or non-periodic.
To provide a robust tool for identifying oscillatory gene expression despite variations in oscillation amplitude and period.
To enable accurate quantification of oscillating cells and their oscillation characteristics within populations.

Main Methods:

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Combines mechanistic stochastic modeling with non-parametric regression using Gaussian processes.
Develops a novel data analysis approach to differentiate between oscillatory and aperiodic gene expression dynamics.
Validates the method using simulated data from a genetic oscillator model and experimental live-cell imaging data.

Main Results:

The new method successfully distinguishes oscillatory gene expression from random fluctuations in single-cell time series.
It outperforms the traditional Lomb-Scargle periodogram in classifying oscillatory versus non-oscillatory cells.
Analysis revealed a significantly higher proportion of oscillating cells under the Hes1 promoter compared to the MMLV promoter in live-cell imaging experiments.

Conclusions:

The developed statistical method offers an objective and powerful approach for analyzing noisy single-cell gene expression data.
It accurately identifies oscillatory dynamics, even with inherent variability, advancing the study of gene networks.
The method is broadly applicable to various gene networks, quantifying oscillations and is publicly available.