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Activity Number: 340 - Novel Methods for Microbiome and Metabolomic Disease
Type: Contributed
Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 2:00 PM
Sponsor: Biometrics Section
Abstract #313987
Title: Differential Abundance Analysis with Bayes Shrinkage Estimation of Variance (DASEV) for Zero-Inflated Proteomic and Metabolomic Data
Author(s): Chi Wang* and Zhengyan Huang and Andrew Lane and Teresa Fan and Rick Higashi and Heidi Weiss and Xiangrong Yin
Companies: University of Kentucky and University of Kentucky and University of Kentucky and University of Kentucky and University of Kentucky and University of Kentucky and University of Kentucky
Keywords: Differential abundance analysis; proteomics; metabolomics; Zero-inflated data

Mass spectrometry (MS) is frequently used for proteomic and metabolomic profiling of biological samples. Data obtained by MS are often zero-inflated. Those zero values are called point mass values (PMVs). Zero values can be further grouped into biological PMVs and technical PMVs. The former type is caused by true absence of a compound and the later type is caused by a technical detection limit. Methods based on a mixture model have been developed to separate the two types of zeros and to perform differential abundance analysis comparing proteomic/metabolomic profiles between different groups of subjects. However, we notice that those methods may give unstable estimate of the model variance, and thus lead to false positive and false negative results when the number of non-zero values is small. We propose a new differential abundance analysis method, DASEV, which uses an empirical Bayes shrinkage method to more robustly estimate the variance and enhance the accuracy of differential abundance analysis. Simulation studies and real data analysis show that DASEV substantially improves parameter estimation of the mixture model and outperforms current methods.

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

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