Work

A Review on Differential Abundance Analysis Methods for Mass Spectrometry-Based Metabolomic Data

Year: 2022

Type: review

Abstract: This review presents an overview of the statistical methods on differential abundance (DA) analysis for mass spectrometry (MS)-based metabolomic data. MS has been widely used for metabolomic abundance profiling in biological samples. The high-throughput data produced by MS often contain a large fraction of zero values caused by the absence of certain metabolites and the technical detection limits of MS. Various statistical methods have been developed to characterize the zero-inflated metabolomic data and perform DA analysis, ranging from simple tests to more complex models including parametric, semi-parametric, and non-parametric approaches. In this article, we discuss and compare DA analysis methods regarding their assumptions and statistical modeling techniques.

Source: Metabolites

DOI: 10.3390/metabo12040305

Authors: Zhengyan Huang, Chi Wang

Institutions: University of Kentucky, Markey Cancer Center

Language: English

Topic: Metabolomics and Mass Spectrometry Studies

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $2,094

Funders: University of Kentucky, National Institutes of Health, National Cancer Institute