Difference between revisions of "Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies"
(→Further outcomes) |
(→Outcome O2) |
||
| Line 15: | Line 15: | ||
==== Outcome O2 ==== | ==== Outcome O2 ==== | ||
| − | There exist MNAR-devoted methods and MCAR-devoted methods (see | + | 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 | + | Depending on the MNAR ratio of a specific data set, one should privilege a MNAR/MCAR-devoted method (see Figure 4). |
==== Outcome O3 ==== | ==== Outcome O3 ==== | ||
Revision as of 09:20, 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
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.
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.
