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Detecting common genomic mechanism between diseases using paired sparse canonical correlation analysis

Human diseases of different etiologies, for example, different acute inflammatory diseases, often share common underlying molecular mechanism. Investigating this commonality will help us not only better understand the diseases but also identify new molecular targets for therapy. In our study, we make use of genomic profile data together with clinical information and propose a statistical approach to detect the common gene set that is reflective of the common mechanism in the multiple diseases. Our paired sparse Canonical Correlation Analysis (psCCA), utilizes multiple clinical variables that measure different aspects of the disease severity or progress. Simultaneously assessing relationships between multiple clinical variables and genomic profiles for given diseases, psCCA provides the overall picture of the relationships, thus is a more adequate approach to explore multiple diseases than performing millions of individual tests. Using lasso penalty, psCCA identifies the sparse linear combination of genes that is highly correlated with the corresponding clinical variables. It maximizes the correlation within diseases and restricts the model such that the selected genes are common for all given diseases. Our simulation shows that psCCA is able to select a common gene set underlying clinical phenotype for multiple diseases. We also demonstrate this new method on data sets of burn and trauma patients.