Difference between revisions of "Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies"
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Webb-Robertson, B.-J. M.; Wiberg, H. K.; Matzke, M. M.; | Webb-Robertson, B.-J. M.; Wiberg, H. K.; Matzke, M. M.; | ||
Brown, J. N.; Wang, J.; McDermott, J. E.; Smith, R. D.; Rodland, K. D.; | Brown, J. N.; Wang, J.; McDermott, J. E.; Smith, R. D.; Rodland, K. D.; | ||
| − | Metz, T. O.; Pounds, J. G.; Waters, K. M.; et al. | + | Metz, T. O.; Pounds, J. G.; Waters, K. M.; et al. Review, evaluation, |
and discussion of the challenges of missing value imputation for mass | and discussion of the challenges of missing value imputation for mass | ||
| − | spectrometry-based label-free global proteomics. | + | spectrometry-based label-free global proteomics. J. Proteome Res. 2015, |
| − | 14 (5), 1993−2001. | + | 14 (5), 1993−2001. [[Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics.]][https://doi.org/10.1021/acs.jproteome.5b00981: DOI] |
Revision as of 09:53, 25 February 2020
Contents
Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies
Lazar, C., Gatto, L., Ferro, M., Bruley, C., and Burger, T. (2016): Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies. Journal of Proteome Research, 15:1116–1125.
https://doi.org/10.1021/acs.jproteome.5b00981
Summary
In this paper 5 imputation algorithms are evaluated depending on the number of missing values and randomness of the data to set practical guideless in choosing an appropriate imputation method which accounts for the specific type of missingness mechanism.
Study outcomes
Following outcomes can be drawn from the paper:
Outcome O1
Imputation performs better with fewer missing values.
Outcome O2
There exist MNAR-devoted methods and MCAR-devoted methods (see Figures 2 and 3). Depending on the MNAR ratio of a specific data set, one should privilege a MNAR/MCAR-devoted method (see Figure 4).
Outcome O3
MNAR-devoted methods perform worse the more missing values and the more random the missing values are (see Figures 2 and 3).
MCAR-devoted methods perform worse the more missing values and the more NOT at random the missing values are (see Figures 2 and 3).
Outcome O4
On average MCAR-devoted methods outperform MNAR-devoted methods, so that MCAR-devoted methods are recommended if the randomness of missing values is not known.
Outcome O5
Peptide-level imputation is more accuarte (Figure 6).
Study design and evidence level
The consideration of simulated data as well as real data, plus the application on protein level as well as on peptide level, makes the result sound and reliable.
The great variations of missing value incorporation, 11 rates of MV and 11 rates of MNAR values, result in 121 simulated datasets which give a broad representation of different missingness mechanisms.
Imputation was performed with 3 MCAR-devoted methods (kNN, SVDimpute, MLE) and 2 MNAR-devoted methods (MinDet, MinProb) which is not many but still shows the performance difference between MCAR/MNAR-devoted methods.
Further comments and aspects
References
Webb-Robertson, B.-J. M.; Wiberg, H. K.; Matzke, M. M.; Brown, J. N.; Wang, J.; McDermott, J. E.; Smith, R. D.; Rodland, K. D.; Metz, T. O.; Pounds, J. G.; Waters, K. M.; et al. Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics. J. Proteome Res. 2015, 14 (5), 1993−2001. Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics.DOI
