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Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)

Published on: March 14, 2013

MetaFIND: a feature analysis tool for metabolomics data.

Kenneth Bryan¹, Lorraine Brennan, Pádraig Cunningham

¹Complex & Adaptive Systems Laboratory (CASL), University College Dublin, Ireland. kenneth.bryan@ucd.ie

BMC Bioinformatics

|November 7, 2008

Summary

This summary is machine-generated.

Metabolomics data analysis can miss key features due to high dimensionality. The MetaFIND tool enhances metabolite signature discovery and correlation analysis after initial feature selection.

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Area of Science:

Metabolomics and Metabonomics
Bioinformatics and Computational Biology

Background:

Metabolomics involves quantitative analysis of metabolites using techniques like NMR spectroscopy or Mass Spectrometry.
Feature selection in metabolomics aims to improve classification accuracy but can be affected by noise and data complexity.
High dimensionality and multi-collinearity in metabolomics data can lead to incomplete feature sets.

Purpose of the Study:

To introduce MetaFIND, an application for post-feature selection correlation analysis in metabolomics.
To address limitations of standard feature selection in complex metabolomics datasets.

Main Methods:

Development and application of the MetaFIND tool for post-feature selection analysis.
Evaluation using two metabolomics datasets with features selected by diverse techniques.

Main Results:

MetaFIND effectively elucidates metabolite signatures from selected features.
The tool aids in discovering additional significant features, including novel class-discriminating metabolites.
MetaFIND supports the identification of higher-level metabolite correlations.

Conclusions:

Standard feature selection may be insufficient for high-dimensional, multi-collinear metabolomics data.
MetaFIND enhances metabolite signature elucidation and feature discovery.
The tool facilitates the inference of metabolic correlations.