Imputation of Missing Values for Multi-Biospecimen Metabolomics Studies: Bias and Effects on Statistical Validity

The analysis of high-throughput metabolomics mass spectrometry data across multiple biological sample types (biospecimens) poses challenges due to missing data. During differential abundance analysis, dropping samples with missing values can lead to severe loss of data as well as biased results in g...

Descripción completa

Detalles Bibliográficos
Autores principales: Wilson, Machelle D., Ponzini, Matthew D., Taylor, Sandra L., Kim, Kyoungmi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9317643/
https://www.ncbi.nlm.nih.gov/pubmed/35888795
http://dx.doi.org/10.3390/metabo12070671

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9317643/
https://www.ncbi.nlm.nih.gov/pubmed/35888795
http://dx.doi.org/10.3390/metabo12070671

Ejemplares similares