Filtering procedures for untargeted LC-MS metabolomics data.

ABSTRACT:

Background

Untargeted metabolomics datasets contain large proportions of uninformative features that can impede subsequent statistical analysis such as biomarker discovery and metabolic pathway analysis. Thus, there is a need for versatile and data-adaptive methods for filtering data prior to investigating the underlying biological phenomena. Here, we propose a data-adaptive pipeline for filtering metabolomics data that are generated by liquid chromatography-mass spectrometry (LC-MS) platforms. Our data-adaptive pipeline includes novel methods for filtering features based on blank samples, proportions of missing values, and estimated intra-class correlation coefficients.

Results

Using metabolomics datasets that were generated in our laboratory from samples of human blood, as

SUBMITTER: Schiffman C 

PROVIDER: S-EPMC6570933 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature