Review, Evaluation, and Discussion of the Challenges of Missing Value Imputation for Mass Spectrometry-Based Label-Free Global Proteomics
Year: 2015
Type: review
Abstract: In this review, we apply selected imputation strategies to label-free liquid chromatography-mass spectrometry (LC-MS) proteomics datasets to evaluate the accuracy with respect to metrics of variance and classification. We evaluate several commonly used imputation approaches for individual merits and discuss the caveats of each approach with respect to the example LC-MS proteomics data. In general, local similarity-based approaches, such as the regularized expectation maximization and least-squares adaptive algorithms, yield the best overall performances with respect to metrics of accuracy and robustness. However, no single algorithm consistently outperforms the remaining approaches, and in some cases, performing classification without imputation sometimes yielded the most accurate classification. Thus, because of the complex mechanisms of missing data in proteomics, which also vary from peptide to protein, no individual method is a single solution for imputation. On the basis of the observations in this review, the goal for imputation in the field of computational proteomics should be to develop new approaches that work generically for this data type and new strategies to guide users in the selection of the best imputation for their dataset and analysis objectives.
Source: Journal of Proteome Research
DOI: 10.1021/pr501138h
Institution: Pacific Northwest National Laboratory
Language: English
Subfield: Spectroscopy
Field: Chemistry
Domain: Physical Sciences
Open Access status: green
Funders: U.S. Department of Energy, Battelle, National Institutes of Health, U.S. Public Health Service, National Cancer Institute, National Institute of General Medical Sciences, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Environmental Health Sciences, National Center for Research Resources, Biological and Environmental Research, Pacific Northwest National Laboratory
Awards: G1095041162, G1563962946, G1828715329, Adv.Proteomics & Metabolomics:Type 2 Diabet & Pre-Diabet, G1897793144, G1907821232, G2506945562, G263956163, Interactive Informatics Resource for Research-driven Cancer Proteomics, G3445870041, A Proteomics Research Resource for Integrative Biology, Center for Novel Biomarkers of Response, G4863164263, Adv.Proteomics & Metabolomics:Type 2 Diabet & Pre-Diabet, G5303665158, G5321382714, G5373841109, G5608475838, G5654972545, G6243784844, G6812713185, G7965472549, A Proteomics Research Resource for Integrative Biology, G8862853988, G9000122217, G9000182343, G9000285276
OpenAlex ID: W2000403772