Multiple Imputation Approaches Applied to the Missing Value Problem in Bottom-Up Proteomics

Analysis of differential abundance in proteomics data sets requires careful application of missing value imputation. Missing abundance values widely vary when performing comparisons across different sample treatments. For example, one would expect a consistent rate of “missing at random” (MAR) acros...

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Detalles Bibliográficos
Autores principales: Gardner, Miranda L., Freitas, Michael A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8431783/
https://www.ncbi.nlm.nih.gov/pubmed/34502557
http://dx.doi.org/10.3390/ijms22179650

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8431783/
https://www.ncbi.nlm.nih.gov/pubmed/34502557
http://dx.doi.org/10.3390/ijms22179650

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