Review, Evaluation, and Discussion of the Challenges of Missing Value Imputation for Mass Spectrometry-Based Label-Free Global Proteomics

• Published in: Journal of proteome research Vol. 14; no. 5; pp. 1993 - 2001
• Main Authors: Webb-Robertson, Bobbie-Jo M, Wiberg, Holli K, Matzke, Melissa M, Brown, Joseph N, Wang, Jing, McDermott, Jason E, Smith, Richard D, Rodland, Karin D, Metz, Thomas O, Pounds, Joel G, Waters, Katrina M
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 01.05.2015; American Chemical Society (ACS)
• Subjects: accuracy; Algorithms; Animals; BASIC BIOLOGICAL SCIENCES; Blood Proteins - analysis; Chromatography, Liquid - statistics & numerical data; classification; Humans; imputation; label free; Lung - chemistry; Mass Spectrometry - statistics & numerical data; MATHEMATICS AND COMPUTING; mean-square error; Mice; peak intensity; Peptides - analysis; Proteomics - methods; Proteomics - statistics & numerical data; label free; accuracy; Imputation; peak intensity; classification; mean-square error
• ISSN: 1535-3893; 1535-3907
• DOI: 10.1021/pr501138h

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.