Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics

Journal of Proteome Research

Volumn 14, Issue 5, 2015, Pages 1993-2001

  Webb Robertson, Bobbie Jo M ^a   Wiberg, Holli K ^a   Matzke, Melissa M ^a   Brown, Joseph N ^a   Wang, Jing ^a   McDermott, Jason E ^a   Smith, Richard D ^a   Rodland, Karin D ^a   Metz, Thomas O ^a   Pounds, Joel G ^a   Waters, Katrina M ^a  

^a PACIFIC NORTHWEST NATIONAL LABORATORY   (United States)

Author keywords

accuracy; classification; Imputation; label free; mean square error; peak intensity

Indexed keywords

ALGORITHM; COMPARATIVE STUDY; EVALUATION STUDY; GENE EXPRESSION; GLUCOSE TOLERANCE; HUMAN; K NEAREST NEIGHBOR; LEAST SQUARES ADAPTIVE IMPUTATION METHOD; LIMIT OF DETECTION; LIQUID CHROMATOGRAPHY; LOCAL LEAST SQUARE IMPUTATION METHOD; MACHINE LEARNING; MASS SPECTROMETRY; MEASUREMENT ACCURACY; MODEL BASED IMPUTATION METHOD; NONHUMAN; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROCEDURES; PROTEOMICS; QUALITATIVE ANALYSIS; QUANTITATIVE ANALYSIS; RANDOM TAIL IMPUTATION METHOD; REGULARIZED EXPECTATION MAXIMIZATION IMPUTATION METHOD; REVIEW; ANIMAL; CHEMISTRY; LUNG; MOUSE; STATISTICS AND NUMERICAL DATA;

PEPTIDE; PLASMA PROTEIN;

ALGORITHMS; ANIMALS; BLOOD PROTEINS; CHROMATOGRAPHY, LIQUID; HUMANS; LUNG; MASS SPECTROMETRY; MICE; PEPTIDES; PROTEOMICS;

EID: 84929657508     PISSN: 15353893     EISSN: 15353907     Source Type: Journal    
DOI: 10.1021/pr501138h     Document Type: Review

References (48)

1. Van Oudenhove, L.; Devreese, B. A review on recent developments in mass spectrometry instrumentation and quantitative tools advancing bacterial proteomics Appl. Microbiol. Biotechnol. 2013, 97, 4749-4762
2. Zhang, A. H.; Sun, H.; Yan, G. L. Serum proteomics in biomedical research: a systematic review Appl. Biochem. Biotechnol. 2013, 170, 774-786
3. Bantscheff, M.; Lemeer, S.; Savitski, M. M. Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present Anal Bioanal Chem. 2012, 404, 939-965
4. Wright, P. C.; Noirel, J.; Ow, S. Y. A review of current proteomics technologies with a survey on their widespread use in reproductive biology investigations Theriogenology 2012, 77, 738-765 e752
5. Parker, C. E.; Pearson, T. W.; Anderson, N. L. Mass-spectrometry-based clinical proteomics-a review and prospective Analyst 2010, 135, 1830-1838
6. Caffrey, R. E. A review of experimental design best practices for proteomics based biomarker discovery: focus on SELDI-TOF Methods Mol. Biol. 2010, 641, 167-183
7. Schulze, W. X.; Usadel, B. Quantitation in mass-spectrometry-based proteomics Annu. Rev. Plant Biol. 2010, 61, 491-516
8. Goh, W. W.; Lee, Y. H.; Chung, M. How advancement in biological network analysis methods empowers proteomics Proteomics 2012, 12, 550-563
9. Goh, W. W.; Sergot, M. J.; Sng, J. C. Comparative network-based recovery analysis and proteomic profiling of neurological changes in valproic acid-treated mice J. Proteome Res. 2013, 12, 2116-2127
10. Waters, K. M.; Pounds, J. G.; Thrall, B. D. Data merging for integrated microarray and proteomic analysis Briefings Funct. Genomics Proteomics 2006, 5, 261-272
11. Aittokallio, T. Dealing with missing values in large-scale studies: microarray data imputation and beyond Briefings Bioinf. 2010, 11, 253-264
12. Albrecht, D.; Kniemeyer, O.; Brakhage, A. A. Missing values in gel-based proteomics Proteomics 2010, 10, 1202-1211
13. Brock, G. N.; Shaffer, J. R.; Blakesley, R. E. Which missing value imputation method to use in expression profiles: a comparative study and two selection schemes BMC Bioinf. 2008, 9, 12
14. Donders, A. R.; van der Heijden, G. J.; Stijnen, T. Review: a gentle introduction to imputation of missing values J. Clin. Epidemiol. 2006, 59, 1087-1091
15. He, Y. Missing data analysis using multiple imputation: getting to the heart of the matter Circulation 2010, 3, 98-105
16. Kim, H.; Golub, G. H.; Park, H. Missing value estimation for DNA microarray gene expression data: local least squares imputation Bioinformatics 2005, 21, 187-198
17. Li, F.; Nie, L.; Wu, G. Prediction and characterization of missing proteomic data in Desulfovibrio vulgaris Comp. Funct. Genomics 2011, 2011, 780973
18. Little, R. J. A.; Rubin, D. B. Statistical Analysis with Missing Data; Wiley & Sons, Inc: New York, 1987.
19. Schneider, T. Analysis of incomplete climate data: estimation of mean values and covariance matrices and imputation of missing values J. Clim. 2001, 14, 853-871
20. Troyanskaya, O.; Cantor, M.; Sherlock, G. Missing value estimation methods for DNA microarrays Bioinformatics 2001, 17, 520-525
21. Tuikkala, J.; Elo, L. L.; Nevalainen, O. S. Missing value imputation improves clustering and interpretation of gene expression microarray data BMC Bioinf. 2008, 9, 202
22. Callister, S. J.; Barry, R. C.; Adkins, J. N. Normalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics J. Proteome Res. 2006, 5, 277-286
23. Oberg, A. L.; Vitek, O. Statistical design of quantitative mass spectrometry-based proteomic experiments J. Proteome Res. 2009, 8, 2144-2156
24. Pavelka, N.; Fournier, M. L.; Swanson, S. K. Statistical similarities between transcriptomics and quantitative shotgun proteomics data Mol. Cell. Proteomics 2008, 7, 631-644
25. Dakna, M.; Harris, K.; Kalousis, A. Addressing the challenge of defining valid proteomic biomarkers and classifiers BMC Bioinf. 2010, 11, 594
26. Karpievitch, Y.; Stanley, J.; Taverner, T. A statistical framework for protein quantitation in bottom-up MS-based proteomics Bioinformatics 2009, 25, 2028-2034
27. Karpievitch, Y. V.; Dabney, A. R.; Smith, R. D. Normalization and missing value imputation for label-free LC-MS analysis BMC BMC Bioinf. 2012, 13, S5
28. Schlatzer, D. M.; Dazard, J. E.; Dharsee, M. Urinary protein profiles in a rat model for diabetic complications Mol. Cell. Proteomics 2009, 8, 2145-2158
29. Tekwe, C. D.; Carroll, R. J.; Dabney, A. R. Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data Bioinformatics 2012, 28, 1998-2003
30. Tuli, L.; Tsai, T. H.; Varghese, R. S. Using a spike-in experiment to evaluate analysis of LC-MS data Proteome Sci. 2012, 10, 13
31. Wang, H.; Fu, Y.; Sun, R. An SVM scorer for more sensitive and reliable peptide identification via tandem mass spectrometry Pac. Symp. Biocomput. 2006, 303-314
32. Webb-Robertson, B. J.; Matzke, M. M.; Metz, T. O. Sequential projection pursuit principal component analysis-dealing with missing data associated with new -omics technologies Biotechniques 2013, 54, 165-168
33. Webb-Robertson, B. J.; McCue, L. A.; Waters, K. M. Combined statistical analyses of peptide intensities and peptide occurrences improves identification of significant peptides from MS-based proteomics data J. Proteome Res. 2010, 9, 5748-5756
34. Schwammle, V.; Leon, I. R.; Jensen, O. N. Assessment and improvement of statistical tools for comparative proteomics analysis of sparse data sets with few experimental replicates J. Proteome Res. 2013, 12, 3874-3883
35. Bo, T. H.; Dysvik, B.; Jonassen, I. LSimpute: accurate estimation of missing values in microarray data with least squares methods Nucleic Acids Res. 2004, 32, e34
36. Oba, S.; Sato, M. A.; Takemasa, I. A Bayesian missing value estimation method for gene expression profile data Bioinformatics 2003, 19, 2088-2096
37. Tipping, M. E.; Bishop, C. M. Probabilistic principal component analysis J. R. Stat. Soc., Ser. B 1999, 61, 611-622
38. Luo, R.; Colangelo, C. M.; Sessa, W. C. Bayesian analysis of iTRAQ data with nonrandom missingness: identification of differentially expressed proteins Stat. Biosci. 2009, 1, 228-245
39. Taverner, T.; Karpievitch, Y. V.; Polpitiya, A. D. DanteR: an extensible R-based tool for quantitative analysis of -omics data Bioinformatics 2012, 28, 2404-2406
40. Clough, T.; Thaminy, S.; Ragg, S. Statistical protein quantification and significance analysis in label-free LC-MS experiments with complex designs BMC Bioinf. 2012, 13, S6
41. Polpitiya, A. D.; Qian, W. J.; Jaitly, N. DAnTE: a statistical tool for quantitative analysis of -omics data Bioinformatics 2008, 24, 1556-1558
42. Deeb, S. J.; D'Souza, R. C.; Cox, J. Super-SILAC allows classification of diffuse large B-cell lymphoma subtypes by their protein expression profiles Mol. Cell. Proteomics 2012, 11, 77-89
43. Hubner, N. C.; Bird, A. W.; Cox, J. Quantitative proteomics combined with BAC TransgeneOmics reveals in vivo protein interactions J. Cell Biol. 2010, 189, 739-754
44. Oh, S.; Kang, D. D.; Brock, G. N. Biological impact of missing-value imputation on downstream analyses of gene expression profiles Bioinformatics 2011, 27, 78-86
45. Webb-Robertson, B. J.; Matzke, M. M.; Datta, S. Bayesian proteoform modeling improves protein quantification of global proteomic measurements Mol. Cell. Proteomics 2014, 13, 3639-3646
46. Matzke, M. M.; Brown, J. N.; Gritsenko, M. A. A comparative analysis of computational approaches to relative protein quantification using peptide peak intensities in label-free LC-MS proteomics experiments Proteomics 2013, 13, 493-503
47. Webb-Robertson, B. J.; Matzke, M. M.; Jacobs, J. M. A statistical selection strategy for normalization procedures in LC-MS proteomics experiments through dataset-dependent ranking of normalization scaling factors Proteomics 2011, 11, 4736-4741
48. Smith, L. M.; Kelleher, N. L. Proteoform: a single term describing protein complexity Nat. Methods. 2013, 10, 186-187