Bayesian profile regression for clustering analysis involving a longitudinal response and explanatory variables

TL;DR

This paper extends Bayesian profile regression to handle longitudinal and multivariate responses, providing a new R package and demonstrating its application in genomics to identify gene co-regulation patterns.

Contribution

The work introduces PReMiuMlongi, an extension of profile regression for longitudinal data, with new models and applications in genomics.

Findings

Identified 4 gene groups with distinct expression trajectories

Demonstrated the method's effectiveness through simulation studies

Applied to yeast data to uncover transcriptional regulation patterns

Abstract

The identification of sets of co-regulated genes that share a common function is a key question of modern genomics. Bayesian profile regression is a semi-supervised mixture modelling approach that makes use of a response to guide inference toward relevant clusterings. Previous applications of profile regression have considered univariate continuous, categorical, and count outcomes. In this work, we extend Bayesian profile regression to cases where the outcome is longitudinal (or multivariate continuous) and provide PReMiuMlongi, an updated version of PReMiuM, the R package for profile regression. We consider multivariate normal and Gaussian process regression response models and provide proof of principle applications to four simulation studies. The model is applied on budding yeast data to identify groups of genes co-regulated during the Saccharomyces cerevisiae cell cycle. We identify 4 distinct groups of genes associated with specific patterns of gene expression trajectories, along with the bound transcriptional factors, likely involved in their co-regulation process.