Abstract 18329: Unsupervised Hierarchical Cluster Analysis of Combined Metabolomic and Proteomic Profiling Data Sets From Participants in a Community-Based Cohort Study

2016 
Introduction: The integration of metabolomic and proteomic profiling data sets from large cohorts promises to identify novel molecular pathways and therapeutic targets in cardiovascular disease. We hypothesized that unsupervised hierarchical cluster analysis (HCA) of combined proteomic and metabolomic data sets would provide a framework for unbiased identification of novel protein-metabolite associations. Methods: We have previously measured 1129 proteins using the aptamer-based Somascan platform (Somalogic, Boulder, CO) and 239 metabolites using liquid chromatography tandem mass spectrometry in archived plasma samples collected from 899 participants of Exam 5 of the Framingham Heart Study Offspring Cohort. Data were normalized and standardized using the inverse transformation. HCA of all proteins and metabolites was performed with absolute Pearson correlation distance, followed by dendrogram formation using average agglomeration. This process was blinded to all clinical and demographic data. Adaptive bra...
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