Data Imputation in Merged Isobaric Labeling-Based Relative Quantification Datasets

  • Nicolai Bjødstrup Palstrøm
  • Rune Matthiesen
  • Hans Christian BeckEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 2051)


The data-dependent acquisition in mass spectrometry-based proteomics combined with quantitative analysis using isobaric labeling (iTRAQ and TMT) inevitably introduces missing values in proteomic experiments where a number of LC-runs are combined, especially in the growing field of shotgun clinical proteomics, where the protein profiles from the proteomics analysis of several hundred patient samples are compared and correlated to clinical traits such as a specific disease or disease treatment in order to link specific outcomes to one or more proteins. In the context of clinical research it is evident that missing values in such datasets reduce the power of the downstream statistical analysis therefore may hampers the linking of the expression of disease traits to the expression of specific proteins that may be useful for prognostic, diagnostic, or predictive purposes. In our study, we tested three data imputation approaches initially developed for microarray data for the imputation of missing values in datasets that are generated by several runs of shotgun proteomic experiments and where the data were relative protein abundances based on isobaric tags (iTRAQ and TMT). Our conclusion is that imputation methods based on k Nearest Neighbors successfully impute missing values in datasets with up to 50% missing values.

Key words

Missing values Clinical proteomics Data imputation Relative quantification Isobaric tags 



Odense University Hospital Research Fund (Grant R22-A1187-B615) is acknowledged for financial support.


  1. 1.
    Kang H (2013) The prevention and handling of the missing data. Korean J Anesthesiol 64:402–406CrossRefGoogle Scholar
  2. 2.
    Beretta L, Santaniello A (2016) Nearest neighbor imputation algorithms: a critical evaluation. BMC Med Inform Decis Mak 16(Suppl 3):74CrossRefGoogle Scholar
  3. 3.
    Lazar C, Gatto L, Ferro M, Bruley C, Burger T (2016) Accounting for the multiple natures of missing values in label-free quantitative proteomics data sets to compare imputation strategies. J Proteome Res 15:1116–1125CrossRefGoogle Scholar
  4. 4.
    Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R et al (2001) Missing value estimation methods for DNA microarrays. Bioinformatics 17:520–525CrossRefGoogle Scholar
  5. 5.
    Beck HC, Jensen LO, Gils C, Ilondo AMM, Frydland M, Hassager C et al (2018) Proteomic discovery and validation of the confounding effect of heparin administration on the analysis of candidate cardiovascular biomarkers. Clin Chem 64:1474–1484CrossRefGoogle Scholar
  6. 6.
    Chich JF, David O, Villers F, Schaeffer B, Lutomski D, Huet S (2007) Statistics for proteomics: experimental design and 2-DE differential analysis. J Chromatogr B Analyt Technol Biomed Life Sci 849:261–272CrossRefGoogle Scholar
  7. 7.
    Webb-Robertson BJ, Wiberg HK, Matzke MM, Brown JN, Wang J, McDermott JE et al (2015) Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics. J Proteome Res 14:1993–2001CrossRefGoogle Scholar
  8. 8.
    Beck HC, Nielsen EC, Matthiesen R, Jensen LH, Sehested M, Finn P et al (2006) Quantitative proteomic analysis of post-translational modifications of human histones. Mol Cell Proteomics 5:1314–1325CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  • Nicolai Bjødstrup Palstrøm
    • 1
  • Rune Matthiesen
    • 2
  • Hans Christian Beck
    • 1
    Email author
  1. 1.Department of Clinical Biochemistry and PharmacologyOdense University HospitalOdense CDenmark
  2. 2.Computational and Experimental Biology Group, CEDOC, Chronic Diseases Research Centre, NOVA Medical School, Faculdade de Ciências MédicasUniversidade NOVA de LisboaLisbonPortugal

Personalised recommendations