Difference between revisions of "Toward a gold standard for benchmarking gene set enrichment analysis"
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=== Summary === | === 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 === | === Study outcomes === | ||
− | |||
==== Outcome O1 ==== | ==== Outcome O1 ==== | ||
The performance of ... | The performance of ... | ||
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==== Further outcomes ==== | ==== Further outcomes ==== | ||
− | + | Runtimes are as follows: | |
+ | |||
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** GSVA | ** 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." | * "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 ==== | ==== Design for Outcome O1 ==== | ||
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=== Further comments and aspects === | === Further comments and aspects === | ||
+ | An R package (GSEABenchmarkeR) is available that seem to enable similar analyses. | ||
=== References === | === References === | ||
The list of cited or related literature is placed here. | The list of cited or related literature is placed here. |
Latest revision as of 15:57, 25 February 2020
__ NUMBEREDHEADINGS__
Contents
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
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.