Influence of missing values substitutes on multivariate analysis of metabolomics data.

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: zer

SUBMITTER: Gromski PS 

PROVIDER: S-EPMC4101515 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature