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

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.