A Two-Part Semiparametric Model for Metabolomics and Proteomics Data

• Activity Number: 239 - Omics II
• Type: Contributed
• Date/Time: Monday, July 30, 2018 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Li Chen* and Yuntong Li and Teresa Fan and Andrew Lane and Woo-Young Kang and Susanne Arnold and Arnold Stromberg and Chi Wang
• 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 and University of Kentucky
• Keywords: Differential abundance analysis; Metabolomics; Proteomics; Semi-parametric log-linear model; Kernel smoothing

Abstract

Identifying differentially abundant features between different experimental conditions is a common goal for many metabolomics and proteomics studies. However, analyzing metabolomics and proteomics data from mass spectrometry is challenging because the data may not be normally distributed and contain a large fraction of zero values. Although several statistical methods have been proposed, they either require data normality assumption, or are inefficient. We propose a new semi-parametric differential abundance analysis method for metabolomics and proteomics data from mass spectrometry. The method considers a two-part model, a logistic regression for the zero proportion and a semi-parametric log-linear model for the non-zero values. Our method is free of distributional assumption and also allows for adjustment of covariates. We propose a kernel-smoothed likelihood method to estimate regression coefficients in the two-part model and construct a likelihood ratio test for differential abundant analysis. Simulations and real data analyses demonstrate that our method outperforms existing methods.