Comparative assessment and novel strategy on methods for imputing proteomics data.

ABSTRACT: Missing values are a major issue in quantitative proteomics analysis. While many methods have been developed for imputing missing values in high-throughput proteomics data, a comparative assessment of imputation accuracy remains inconclusive, mainly because mechanisms contributing to true missing values are complex and existing evaluation methodologies are imperfect. Moreover, few studies have provided an outlook of future methodological development. We first re-evaluate the performance of eight representative methods targeting three typical missing mechanisms. These methods are compared on both simulated and masked missing values embedded within real proteomics datasets, and performance is evaluated using three quantitative measures. We then introduce fused regularization matrix factoriza

SUBMITTER: Shen M 

PROVIDER: S-EPMC8776850 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature