Addressing Missing Data in GC × GC Metabolomics: Identifying Missingness Type and Evaluating the Impact of Imputation Methods on Experimental Replication

[Image: see text] Missing data is a significant issue in metabolomics that is often neglected when conducting data preprocessing, particularly when it comes to imputation. This can have serious implications for downstream statistical analyses and lead to misleading or uninterpretable inferences. In...

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Detalles Bibliográficos
Autores principales: Davis, Trenton J., Firzli, Tarek R., Higgins Keppler, Emily A., Richardson, Matthew, Bean, Heather D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9369014/
https://www.ncbi.nlm.nih.gov/pubmed/35881554
http://dx.doi.org/10.1021/acs.analchem.1c04093

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9369014/
https://www.ncbi.nlm.nih.gov/pubmed/35881554
http://dx.doi.org/10.1021/acs.analchem.1c04093

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