Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies

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

List the paper results concerning method comparison and benchmarking:

Outcome O1

Imputation performs better with fewer missing values.

Outcome O2

There exist MNAR-devoted methods and MCAR-devoted methods (see Figure 2 and 3). Depending on the MNAR ratio of a specific data set, one should privilege a MNAR/MCAR-devoted method, even if on average they perform worse.

Outcome On

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).

Further outcomes

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Study design and evidence level

General aspects

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Design for Outcome O1

• The outcome was generated for ...
• Configuration parameters were chosen ...
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Design for Outcome O2

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Design for Outcome O

• The outcome was generated for ...
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References

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