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Activity Number: 262
Type: Contributed
Date/Time: Monday, August 1, 2016 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #320915
Title: Multivariate Two-Part Statistics for Analysis of Correlated Mass Spectrometry Data from Multiple Biological Matrices
Author(s): Kyoungmi Kim* and Sandra L. Taylor
Companies: University of California at Davis and University of California at Davis
Keywords: mass spectrometry ; two-part statistics ; missing data ; multivariate analysis ; between-matrix correlation

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.

Authors who are presenting talks have a * after their name.

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