Influence of Missing Values Substitutes on Multivariate Analysis of Metabolomics Data

Missing values are known to be problematic for the analysis of gas chromatography-mass spectrometry (GC-MS) metabolomics data. Typically these values cover about 10%–20% of all data and can originate from various backgrounds, including analytical, computational, as well as biological. Currently, the...

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Main Authors:	Gromski, Piotr S., Xu, Yun, Kotze, Helen L., Correa, Elon, Ellis, David I., Armitage, Emily Grace, Turner, Michael L., Goodacre, Royston
Format:	Online
Language:	English
Published:	MDPI 2014
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4101515/

id	pubmed-4101515
recordtype	oai_dc
spelling	pubmed-41015152014-07-17 Influence of Missing Values Substitutes on Multivariate Analysis of Metabolomics Data Gromski, Piotr S. Xu, Yun Kotze, Helen L. Correa, Elon Ellis, David I. Armitage, Emily Grace Turner, Michael L. Goodacre, Royston Article Missing values are known to be problematic for the analysis of gas chromatography-mass spectrometry (GC-MS) metabolomics data. Typically these values cover about 10%–20% of all data and can originate from various backgrounds, including analytical, computational, as well as biological. Currently, the most well known substitute for missing values is a mean imputation. In fact, some researchers consider this aspect of data analysis in their metabolomics pipeline as so routine that they do not even mention using this replacement approach. However, this may have a significant influence on the data analysis output(s) and might be highly sensitive to the distribution of samples between different classes. Therefore, in this study we have analysed different substitutes of missing values namely: zero, mean, median, k-nearest neighbours (kNN) and random forest (RF) imputation, in terms of their influence on unsupervised and supervised learning and, thus, their impact on the final output(s) in terms of biological interpretation. These comparisons have been demonstrated both visually and computationally (classification rate) to support our findings. The results show that the selection of the replacement methods to impute missing values may have a considerable effect on the classification accuracy, if performed incorrectly this may negatively influence the biomarkers selected for an early disease diagnosis or identification of cancer related metabolites. In the case of GC-MS metabolomics data studied here our findings recommend that RF should be favored as an imputation of missing value over the other tested methods. This approach displayed excellent results in terms of classification rate for both supervised methods namely: principal components-linear discriminant analysis (PC-LDA) (98.02%) and partial least squares-discriminant analysis (PLS-DA) (97.96%) outperforming other imputation methods. MDPI 2014-06-16 /pmc/articles/PMC4101515/ /pubmed/24957035 http://dx.doi.org/10.3390/metabo4020433 Text en © 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Gromski, Piotr S. Xu, Yun Kotze, Helen L. Correa, Elon Ellis, David I. Armitage, Emily Grace Turner, Michael L. Goodacre, Royston
spellingShingle	Gromski, Piotr S. Xu, Yun Kotze, Helen L. Correa, Elon Ellis, David I. Armitage, Emily Grace Turner, Michael L. Goodacre, Royston Influence of Missing Values Substitutes on Multivariate Analysis of Metabolomics Data
author_facet	Gromski, Piotr S. Xu, Yun Kotze, Helen L. Correa, Elon Ellis, David I. Armitage, Emily Grace Turner, Michael L. Goodacre, Royston
author_sort	Gromski, Piotr S.
title	Influence of Missing Values Substitutes on Multivariate Analysis of Metabolomics Data
title_short	Influence of Missing Values Substitutes on Multivariate Analysis of Metabolomics Data
title_full	Influence of Missing Values Substitutes on Multivariate Analysis of Metabolomics Data
title_fullStr	Influence of Missing Values Substitutes on Multivariate Analysis of Metabolomics Data
title_full_unstemmed	Influence of Missing Values Substitutes on Multivariate Analysis of Metabolomics Data
title_sort	influence of missing values substitutes on multivariate analysis of metabolomics data
description	Missing values are known to be problematic for the analysis of gas chromatography-mass spectrometry (GC-MS) metabolomics data. Typically these values cover about 10%–20% of all data and can originate from various backgrounds, including analytical, computational, as well as biological. Currently, the most well known substitute for missing values is a mean imputation. In fact, some researchers consider this aspect of data analysis in their metabolomics pipeline as so routine that they do not even mention using this replacement approach. However, this may have a significant influence on the data analysis output(s) and might be highly sensitive to the distribution of samples between different classes. Therefore, in this study we have analysed different substitutes of missing values namely: zero, mean, median, k-nearest neighbours (kNN) and random forest (RF) imputation, in terms of their influence on unsupervised and supervised learning and, thus, their impact on the final output(s) in terms of biological interpretation. These comparisons have been demonstrated both visually and computationally (classification rate) to support our findings. The results show that the selection of the replacement methods to impute missing values may have a considerable effect on the classification accuracy, if performed incorrectly this may negatively influence the biomarkers selected for an early disease diagnosis or identification of cancer related metabolites. In the case of GC-MS metabolomics data studied here our findings recommend that RF should be favored as an imputation of missing value over the other tested methods. This approach displayed excellent results in terms of classification rate for both supervised methods namely: principal components-linear discriminant analysis (PC-LDA) (98.02%) and partial least squares-discriminant analysis (PLS-DA) (97.96%) outperforming other imputation methods.
publisher	MDPI
publishDate	2014
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4101515/
_version_	1613115019984109568

Influence of Missing Values Substitutes on Multivariate Analysis of Metabolomics Data

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