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Identifying relationships between molecular variations and their clinicalpresentations has been challenged by the heterogeneous causes of a disease. Itis imperative to unveil the relationship between the high dimensional molecularmanifestations and the clinical presentations, while taking into account thepossible heterogeneity of the study subjects. We proposed a novel supervisedclustering algorithm using penalized mixture regression model, called CSMR, todeal with the challenges in studying the heterogeneous relationships betweenhigh dimensional molecular features to a phenotype. The algorithm was adaptedfrom the classification expectation maximization algorithm, which offers anovel supervised solution to the clustering problem, with substantialimprovement on both the computational efficiency and biologicalinterpretability. Experimental evaluation on simulated benchmark datasetsdemonstrated that the CSMR can accurately identify the subspaces on whichsubset of features are explanatory to the response variables, and itoutperformed the baseline methods. Application of CSMR on a drug sensitivitydataset again demonstrated the superior performance of CSMR over the others,where CSMR is powerful in recapitulating the distinct subgroups hidden in thepool of cell lines with regards to their coping mechanisms to different drugs.CSMR represents a big data analysis tool with the potential to resolve thecomplexity of translating the clinical manifestations of the disease to thereal causes underpinning it. We believe that it will bring new understanding tothe molecular basis of a disease, and could be of special relevance in thegrowing field of personalized medicine.

Supervised clustering of high dimensional data using regularized mixture modeling