SDA: a semi-parametric differential abundance analysis method for metabolomics and proteomics data.

ABSTRACT: BACKGROUND:Identifying differentially abundant features between different experimental groups is a common goal for many metabolomics and proteomics studies. However, analyzing data from mass spectrometry (MS) is difficult because the data may not be normally distributed and there is often a large fraction of zero values. Although several statistical methods have been proposed, they either require the data normality assumption or are inefficient. RESULTS:We propose a new semi-parametric differential abundance analysis (SDA) method for metabolomics and proteomics data from MS. The method considers a two-part model, a logistic regression for the zero proportion and a semi-parametric log-linear model for the possibly non-normally distributed non-zero values, to characterize data from each feat

SUBMITTER: Li Y 

PROVIDER: S-EPMC6798423 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature