View source for 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: 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 ====
...

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.