MetaClean: a machine learning-based classifier for reduced false positive peak detection in untargeted LC-MS metabolomics data.

ABSTRACT:

Introduction

Despite the availability of several pre-processing software, poor peak integration remains a prevalent problem in untargeted metabolomics data generated using liquid chromatography high-resolution mass spectrometry (LC-MS). As a result, the output of these pre-processing software may retain incorrectly calculated metabolite abundances that can perpetuate in downstream analyses.

Objectives

To address this problem, we propose a computational methodology that combines machine learning and peak quality metrics to filter out low quality peaks.

Methods

Specifically, we comprehensively and systematically compared the performance of 24 different classifiers generated by combining eight classification algorithms and three sets of peak quality metrics on the task o

SUBMITTER: Chetnik K 

PROVIDER: S-EPMC7895495 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature