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Metabolites 2014, 4(2), 433-452; doi:10.3390/metabo4020433
Article

Influence of Missing Values Substitutes on Multivariate Analysis of Metabolomics Data

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Received: 14 April 2014; in revised form: 29 May 2014 / Accepted: 5 June 2014 / Published: 16 June 2014
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Abstract: 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.
Keywords: missing values; metabolomics; unsupervised learning; supervised learning missing values; metabolomics; unsupervised learning; supervised learning
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Gromski, P.S.; Xu, Y.; Kotze, H.L.; Correa, E.; Ellis, D.I.; Armitage, E.G.; Turner, M.L.; Goodacre, R. Influence of Missing Values Substitutes on Multivariate Analysis of Metabolomics Data. Metabolites 2014, 4, 433-452.

AMA Style

Gromski PS, Xu Y, Kotze HL, Correa E, Ellis DI, Armitage EG, Turner ML, Goodacre R. Influence of Missing Values Substitutes on Multivariate Analysis of Metabolomics Data. Metabolites. 2014; 4(2):433-452.

Chicago/Turabian Style

Gromski, Piotr S.; Xu, Yun; Kotze, Helen L.; Correa, Elon; Ellis, David I.; Armitage, Emily G.; Turner, Michael L.; Goodacre, Royston. 2014. "Influence of Missing Values Substitutes on Multivariate Analysis of Metabolomics Data." Metabolites 4, no. 2: 433-452.



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