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The discovery of disease subtypes is an essential step for developingprecision medicine, and disease subtyping via omics data has become a popularapproach. While promising, subtypes obtained from conventional approaches maynot be necessarily associated with clinical outcomes. The collection of richclinical data along with omics data has provided an unprecedented opportunityto facilitate the disease subtyping process and to discovery clinicallymeaningful disease subtypes. Thus, we developed an outcome-guided Bayesianclustering (GuidedBayesianClustering) method to fully integrate the clinicaldata and the high-dimensional omics data. A Gaussian mixed model framework wasapplied to perform sample clustering; a spike-and-slab prior was utilized toperform gene selection; a mixture model prior was employed to incorporate theguidance from a clinical outcome variable; and a decision framework was adoptedto infer the false discovery rate of the selected genes. We deployed conjugatepriors to facilitate efficient Gibbs sampling. Our proposed full Bayesianmethod is capable of simultaneously (i) obtaining sample clustering (diseasesubtype discovery); (ii) performing feature selection (select genes related tothe disease subtype); and (iii) utilizing clinical outcome variable to guidethe disease subtype discovery. The superior performance of theGuidedBayesianClustering was demonstrated through simulations and applicationsof breast cancer expression data.

Outcome-guided Bayesian Clustering for Disease Subtype Discovery Using High-dimensional Transcriptomic Data