Longitudinal analysis of gene expression profiles using functional mixed-effects models

TL;DR

This paper introduces a functional mixed-effects model for analyzing short, high-dimensional longitudinal gene expression data, accounting for variability and missing values, and includes a statistical test for differential expression.

Contribution

It presents a novel functional mixed-effects modeling approach tailored for complex longitudinal gene expression data with missing values and variability.

Findings

Model effectively captures temporal gene expression patterns.

Statistical test accurately detects differential expression.

Simulation and real data demonstrate model's robustness.

Abstract

In many longitudinal microarray studies, the gene expression levels in a random sample are observed repeatedly over time under two or more conditions. The resulting time courses are generally very short, high-dimensional, and may have missing values. Moreover, for every gene, a certain amount of variability in the temporal profiles, among biological replicates, is generally observed. We propose a functional mixed-effects model for estimating the temporal pattern of each gene, which is assumed to be a smooth function. A statistical test based on the distance between the fitted curves is then carried out to detect differential expression. A simulation procedure for assessing the statistical power of our model is also suggested. We evaluate the model performance using both simulations and a real data set investigating the human host response to BCG exposure.