High-dimensional Bayesian Fourier Analysis For Detecting Circadian Gene Expressions

TL;DR

This paper introduces a Bayesian Fourier analysis method that models dependence across genes to improve detection of circadian gene expressions, enabling more accurate identification of clock-controlled genes in time-course data.

Contribution

It develops a novel Bayesian approach using latent factors and Fourier domain analysis to account for gene dependence in circadian rhythm detection.

Findings

Successfully applied to mouse liver data

Improves detection accuracy over existing methods

Handles complex dependence in functional data

Abstract

In genomic applications, there is often interest in identifying genes whose time-course expression trajectories exhibit periodic oscillations with a period of approximately 24 hours. Such genes are usually referred to as circadian, and their identification is a crucial step toward discovering physiological processes that are clock-controlled. It is natural to expect that the expression of gene i at time j might depend to some degree on the expression of the other genes measured at the same time. However, widely-used rhythmicity detection techniques do not accommodate for the potential dependence across genes. We develop a Bayesian approach for periodicity identification that explicitly takes into account the complex dependence structure across time-course trajectories in gene expressions. We employ a latent factor representation to accommodate dependence, while representing the true trajectories in the Fourier domain allows for inference on period, phase, and amplitude of the signal. Identification of circadian genes is allowed through a carefully chosen variable selection prior on the Fourier basis coefficients. The methodology is applied to a novel mouse liver circadian dataset. Although motivated by time-course gene expression array data, the proposed approach is applicable to the analysis of dependent functional data at broad.