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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Revision as of 09:01, 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
List the paper results concerning method comparison and benchmarking:
Outcome O1
The performance of ...
Outcome O1 is presented as Figure X in the original publication.
Outcome O2
...
Outcome O2 is presented as Figure X in the original publication.
Outcome On
...
Outcome On is presented as Figure X in the original publication.
Further outcomes
If intended, you can add further outcomes here.
Study design and evidence level
General aspects
You can describe general design aspects here. The study designs for describing specific outcomes are listed in the following subsections:
Design for Outcome O1
- The outcome was generated for ...
- Configuration parameters were chosen ...
- ...
Design for Outcome O2
- The outcome was generated for ...
- Configuration parameters were chosen ...
- ...
...
Design for Outcome O
- The outcome was generated for ...
- Configuration parameters were chosen ...
- ...
Further comments and aspects
References
The list of cited or related literature is placed here.
