View source for Toward a gold standard for benchmarking gene set enrichment analysis

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=== Citation ===
Geistlinger, L., Csaba, G., Santarelli, M., Ramos, M., Schiffer, L., Law, C., ... & Zimmer, R., Toward a gold standard for benchmarking gene set enrichment analysis, 2020, Bioinformatics, 0, 1-12 

[https://doi.org/10.1093/bib/bbz158 Permanent link to the paper]

=== Summary ===
Gene set analyses are combination of several analysis modules. 
This paper investigates the performance of ten prominent approaches.
Biological plausibility based on co-citation databases is used for assessment.

=== Study outcomes ===
==== 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 ====
Runtimes are as follows:



=== Study design and evidence level ===
==== General aspects ====
* "75 expression datasets investigating 42 human diseases"
* microarray and RNAseq data
* pre-existing benchmark data sets
* 10 methods:
** ORA
** GLOBALTEST
** GSEA
** SAFE
** GSA
** SAMGS
** ROAST
** CAMERA
** PADOG
** GSVA
* "Gene set relevance rankings for each disease were constructed by querying the MalaCards database. MalaCards scores genes for disease relevance based on experimental evidence and co-citation in the literature."
* "A nominal significance level of 0.05" is used (without correction with respect to multiple testing). This was also common in other benchmark studies.
* The "type I error rate was evaluated by randomization of the sample labels" of the microarray data set.
* "Random gene sets of increasing set size were analyzed to assess whether enrichment methods are affected by geneset size." For this purpose, 100 "random gene sets of defined sizes {5,10,25,50,100,250,500}" were sampled.

==== 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 ===
An R package (GSEABenchmarkeR) is available that seem to enable similar analyses. 

=== References ===
The list of cited or related literature is placed here.