A Review of Imputation Strategies for Isobaric Labeling-Based Shotgun Proteomics

The throughput efficiency and increased depth of coverage provided by isobaric-labeled proteomics measurements have led to increased usage of these techniques. However, the structure of missing data is different than unlabeled studies, which prompts the need for this review to compare the efficacy of nine imputation methods on large isobaric-labeled proteomics data sets to guide researchers on the appropriateness of various imputation methods. Imputation methods were evaluated by accuracy, statistical hypothesis test inference, and run time. In general, expectation maximization and random forest imputation methods yielded the best performance, and constant-based methods consistently performed poorly across all data set sizes and percentages of missing values. For data sets with small sample sizes and higher percentages of missing data, results indicate that statistical inference with no imputation may be preferable. On the basis of the findings in this review, there are core imputation methods that perform better for isobaric-labeled proteomics data, but great care and consideration as to whether imputation is the optimal strategy should be given for data sets comprised of a small number of samples.

Automated summary

The study compared the efficacy of nine imputation methods on large isobaric-labeled proteomics data sets and found that expectation maximization and random forest imputation methods performed the best. It also suggested that statistical inference without imputation may be preferable for data sets with small sample sizes and higher percentages of missing data.