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The identification of sets of co-regulated genes that share a common functionis a key question of modern genomics. Bayesian profile regression is asemi-supervised mixture modelling approach that makes use of a response toguide inference toward relevant clusterings. Previous applications of profileregression have considered univariate continuous, categorical, and countoutcomes. In this work, we extend Bayesian profile regression to cases wherethe outcome is longitudinal (or multivariate continuous) and providePReMiuMlongi, an updated version of PReMiuM, the R package for profileregression. We consider multivariate normal and Gaussian process regressionresponse models and provide proof of principle applications to four simulationstudies. The model is applied on budding yeast data to identify groups of genesco-regulated during the Saccharomyces cerevisiae cell cycle. We identify 4distinct groups of genes associated with specific patterns of gene expressiontrajectories, along with the bound transcriptional factors, likely involved intheir co-regulation process.

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