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Abstract:
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High through-put mass spectrometry (MS) is now being used to profile small molecule compounds across multiple biological sample types (i.e., matrices) from the same subjects with the goal of leveraging information across matrices. Multivariate statistical methods that combine information from all biological matrices could be more powerful than the usual univariate analyses. However, missing values are common in MS data and imputation can impact between-matrix correlation and multivariate analysis results. We propose two multivariate two-part statistics that accommodate missing values and combine data from all biological matrices to identify differentially-regulated compounds.
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