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:	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/

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