Adaptive binning: an improved binning method for metabolomics data using the undecimated wavelet transform

Richard Davis; Adrian Charlton; John Godward; Stephen Jones; Mark Harrison; J.C. Wilson

doi:10.1016/j.chemolab.2006.08.014

Adaptive binning: an improved binning method for metabolomics data using the undecimated wavelet transform

Richard Davis, Adrian Charlton, John Godward, Stephen Jones, Mark Harrison, J.C. Wilson

Research output: Contribution to journal › Article › peer-review

Abstract

Statistical analysis of metabolomic datasets can lead to erroneous interpretation of results due to misalignment of the data. Therefore pre-processing methods for peak alignment and data averaging (binning or bucketing) to improve data quality have been used. Here we introduce adaptive binning. The undecimated wavelet transform is used in an improved method for correcting variation in chemical shifts in nuclear magnetic resonance spectroscopy data. Adaptive binning using theoretical and metabolomics NMR spectra significantly increases the ratio of inter-class to intra-class variation and increases data interpretability when compared to conventional binning.

Original language	English
Pages (from-to)	144-154
Number of pages	10
Journal	Chemometrics and Intelligent Laboratory Systems
Volume	85
Issue number	1
DOIs	https://doi.org/10.1016/j.chemolab.2006.08.014
Publication status	Published - Jan 2007

Keywords

Binning;
Wavelets;
Metabolomics;
Metabonomics;
NMR

Access to Document

10.1016/j.chemolab.2006.08.014

http://dx.doi.org/10.1016/j.chemolab.2006.08.014

Cite this

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title = "Adaptive binning: an improved binning method for metabolomics data using the undecimated wavelet transform",

abstract = "Statistical analysis of metabolomic datasets can lead to erroneous interpretation of results due to misalignment of the data. Therefore pre-processing methods for peak alignment and data averaging (binning or bucketing) to improve data quality have been used. Here we introduce adaptive binning. The undecimated wavelet transform is used in an improved method for correcting variation in chemical shifts in nuclear magnetic resonance spectroscopy data. Adaptive binning using theoretical and metabolomics NMR spectra significantly increases the ratio of inter-class to intra-class variation and increases data interpretability when compared to conventional binning.",

keywords = "Binning; , Wavelets; , Metabolomics; , Metabonomics; , NMR",

author = "Richard Davis and Adrian Charlton and John Godward and Stephen Jones and Mark Harrison and J.C. Wilson",

year = "2007",

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doi = "10.1016/j.chemolab.2006.08.014",

language = "English",

volume = "85",

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journal = "Chemometrics and Intelligent Laboratory Systems",

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AU - Davis, Richard

AU - Charlton, Adrian

AU - Godward, John

AU - Jones, Stephen

AU - Harrison, Mark

AU - Wilson, J.C.

PY - 2007/1

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AB - Statistical analysis of metabolomic datasets can lead to erroneous interpretation of results due to misalignment of the data. Therefore pre-processing methods for peak alignment and data averaging (binning or bucketing) to improve data quality have been used. Here we introduce adaptive binning. The undecimated wavelet transform is used in an improved method for correcting variation in chemical shifts in nuclear magnetic resonance spectroscopy data. Adaptive binning using theoretical and metabolomics NMR spectra significantly increases the ratio of inter-class to intra-class variation and increases data interpretability when compared to conventional binning.

KW - Binning;

KW - Wavelets;

KW - Metabolomics;

KW - Metabonomics;

KW - NMR

U2 - 10.1016/j.chemolab.2006.08.014

DO - 10.1016/j.chemolab.2006.08.014

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JO - Chemometrics and Intelligent Laboratory Systems

JF - Chemometrics and Intelligent Laboratory Systems

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ER -