Influence of Missing Values Substitutes on Multivariate Analysis of Metabolomics Data

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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Bibliographic Details
Main Authors: Piotr S. Gromski, Yun Xu, Helen L. Kotze, Elon Correa, David I. Ellis, Emily Grace Armitage, Michael L. Turner, Royston Goodacre
Format: Article
Language:English
Published: MDPI AG 2014-06-01
Series:Metabolites
Subjects:
missing values
metabolomics
unsupervised learning
supervised learning
Online Access:http://www.mdpi.com/2218-1989/4/2/433

Internet

http://www.mdpi.com/2218-1989/4/2/433

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