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Issue 28, 2020
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An approach for feature selection with data modelling in LC-MS metabolomics

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Abstract

The data processing workflow for LC-MS based metabolomics study is suggested with signal drift correction, univariate analysis, supervised learning, feature selection and unsupervised modelling. The proposed approach requires only an annotation-free peak table and produces an extremely reduced set of the most relevant features together with validation via Receiver Operating Characteristic analysis for selected predictors, cross-validation and unsupervised projection. The presented study was initially optimised by its own experimental set and then was successfully tested by using 36 datasets from 21 publicly available metabolomics projects. The suggested workflow can be used for classification purposes in high dimensional metabolomics studies and as a first step in exploratory analysis, data projection, biomarker selection, data integration and fusion.

Graphical abstract: An approach for feature selection with data modelling in LC-MS metabolomics

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Supplementary files

Article information


Submitted
31 Jan 2020
Accepted
12 Jun 2020
First published
15 Jun 2020

Anal. Methods, 2020,12, 3582-3591
Article type
Paper

An approach for feature selection with data modelling in LC-MS metabolomics

I. Plyushchenko, D. Shakhmatov, T. Bolotnik, T. Baygildiev, P. N. Nesterenko and I. Rodin, Anal. Methods, 2020, 12, 3582
DOI: 10.1039/D0AY00204F

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