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

ABSTRACT: 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) across batches of samples and varying rates of "missing not at random" (MNAR) depending on the inherent difference in sample treatments within the study. The missing value imputation strategy must thus be selected that best accounts for both MAR and MNAR simultaneously. Several important issues must be considered when deciding the appropriate missing value imputation strategy: (1) when it is appropriate to impute data; (2) how to choose a method that reflects the combinatorial manner of MAR and MNAR

SUBMITTER: Gardner ML 

PROVIDER: S-EPMC8431783 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature