Removal of false positive features to generate authentic peak table for high-resolution mass spectrometry-based metabolomics study.

In this study, a method to remove false positive features (rFPF) was developed to improve the quality of the peak table. rFPF recognizes real peak profiles based on the information entropy and statistical correlation, and eliminates false positive features from non-sample sources and noises. A standard mixture with 42 standards (14 isotopic labeled internal standards and 28 common standards) and a urine sample were applied to evaluate the effectiveness of the rFPF method. The analysis results of metabolite standards showed that more than 92% false positive features were removed by rFPF, but target standards completely remained. The analysis results of urine sample showed that the number of features was significantly reduced from 7182 to 2522. Interestingly, 98% of the identified metabolites remained after removing false positive features. The proposed rFPF shows great prospects as a new data handling method for metabolomics studies. The MATLAB code and data can be downloaded from http://app.ifc.dicp.ac.cn/Confirmation/Authentication.html.
PMID: 31047152 [PubMed - in process]

Source: Analytica Chimica Acta - May 4, 2019 Category: Chemistry Authors: Ju R, Liu X, Zheng F, Zhao X, Lu X, Zeng Z, Lin X, Xu G Tags: Anal Chim Acta Source Type: research