Functional regression clustering with multiple functional gene expressions

TL;DR

This paper introduces a novel clustering method for time series gene expression data that groups genes with similar functional relationships over time, even if their individual profiles differ, revealing biologically meaningful clusters.

Contribution

It develops a K-means type algorithm based on function-on-function regression models that handle multiple functional explanatory variables, advancing gene expression clustering techniques.

Findings

Clusters are enriched for genes with similar biological functions.

The method identifies gene groups with similar diurnal pattern perturbations.

Biological insights include clusters related to photosynthesis and ribosomes.

Abstract

Gene expression data is often collected in time series experiments, under different experimental conditions. There may be genes that have very different gene expression profiles over time, but that adjust their gene expression patterns in the same way under experimental conditions. Our aim is to develop a method that finds clusters of genes in which the relationship between these temporal gene expression profiles are similar to one another, even if the individual temporal gene expression profiles differ. We propose a $K$-means type algorithm in which each cluster is defined by a function-on-function regression model, which, inter alia, allows for multiple functional explanatory variables. We validate this novel approach through extensive simulations and then apply it to identify groups of genes whose diurnal expression pattern is perturbed by the season in a similar way. Our clusters are enriched for genes with similar biological functions, including one cluster enriched in both photosynthesis-related functions and polysomal ribosomes, which shows that our method provides useful and novel biological insights.