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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Detalles Bibliográficos
Autores principales: Gromski, Piotr S., Xu, Yun, Kotze, Helen L., Correa, Elon, Ellis, David I., Armitage, Emily Grace, Turner, Michael L., Goodacre, Royston
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2014
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4101515/
https://www.ncbi.nlm.nih.gov/pubmed/24957035
http://dx.doi.org/10.3390/metabo4020433

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4101515/
https://www.ncbi.nlm.nih.gov/pubmed/24957035
http://dx.doi.org/10.3390/metabo4020433

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