Comparison of imputation and imputation-free methods for statistical analysis of mass spectrometry data with missing data.

ABSTRACT: Missing values are common in high-throughput mass spectrometry data. Two strategies are available to address missing values: (i) eliminate or impute the missing values and apply statistical methods that require complete data and (ii) use statistical methods that specifically account for missing values without imputation (imputation-free methods). This study reviews the effect of sample size and percentage of missing values on statistical inference for multiple methods under these two strategies. With increasing missingness, the ability of imputation and imputation-free methods to identify differentially and non-differentially regulated compounds in a two-group comparison study declined. Random forest and k-nearest neighbor imputation combined with a Wilcoxon test performed well in statisti

SUBMITTER: Taylor S 

PROVIDER: S-EPMC8769695 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature