Application of Gaussian processes in circadian rhythm detection

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Journal Title

Journal ISSN

Volume Title

Perustieteiden korkeakoulu | Master's thesis

Authors

Date

2023-10-09

Department

Major/Subject

Bioinformatics and Digital Health

Mcode

SCI25

Degree programme

Master’s Programme in Life Science Technologies

Language

en

Pages

5+53

Series

Abstract

Circadian rhythms are internal time-keeping mechanisms of many organisms that synchronize behavior, metabolism, and other physiological features with the light-dark cycle of the Earth. Early studies of this process have characterized circadian clock genes and their presence throughout the body of mammals. To better capture the global circadian gene expression in genome-scale datasets, a number of models have been implemented to identify the signal periodicity and detect genes with circadian features. Here, we show that Gaussian processes can also identify rhythmic features (e.g, period, phase, amplitude) by utilizing periodic squared exponential kernels. In comparison with JTK CYCLE and RAIN, two commonly used non-parametric methods for detecting cycling oscillators, Gaussian processes are able to achieve similar results, despite having slightly weaker performance and greater sensitivity to noises. With this method, we validated the mechanism of core circadian clocks as well as their circadian expression in different organs from a public mouse atlas data. We also demonstrate the abruption of 12-hour circadian genes in the liver of mice with liver-specific ablation of X-box binding protein 1 (XBP1), which reproduces the conclusions reported in the literature.

Description

Supervisor

Vehtari, Aki

Thesis advisor

Cheng, Lu

Keywords

circadian rhythms, Gaussian process, statistical methods, peripheral clocks, RNA-seq, genomics

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Citation

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-202310156332

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[dipl] Perustieteiden korkeakoulu / SCI