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

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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

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Outcome O1

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Outcome O1 is presented as Figure X in the original publication.

Outcome O2

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Outcome O2 is presented as Figure X in the original publication.

Outcome On

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Outcome On is presented as Figure X in the original publication.

Further outcomes

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Study design and evidence level

General aspects

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Design for Outcome O1

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  • Configuration parameters were chosen ...
  • ...

Design for Outcome O2

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Design for Outcome O

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  • Configuration parameters were chosen ...
  • ...

Further comments and aspects

References

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