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