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.
|State||Published - Apr 2022|
Bibliographical noteFunding Information:
This research was supported by the National Cancer Institute (R03CA211835) and the Biostatistics and Bioinformatics Shared Resource Facility of the University of Kentucky Markey Cancer Center (P30CA177558).
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
- differential abundance
- mass spectrometry
- zero-inflated data
ASJC Scopus subject areas
- Endocrinology, Diabetes and Metabolism
- Molecular Biology