Gene set analysis methods: a systematic comparison

1 Gene set analysis methods: a systematic comparison

Mathur, R., Rotroff, D., Ma, J., Shojaie, A., & Motsinger-Reif, A. , Gene set analysis methods: a systematic comparison, 2018, BioData mining, 11(1), 8.

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1.1 Summary

Approaches for gene set analyses were assessed by using simulated data that were generated based on a real experimental data set.

1.2 Study outcomes

1.2.1 Outcome O1: False positives under null distribution

The frequency of false-positives was assessed by using an alpha=0.05. Consequently all approaches (except FET-1k) showed around 5% false-positive or less. FET-1k ("FET global statistic in SAFE") had around than 20%.

Outcome O1 is presented as Figure 2 in the original publication for the prostate data template and in the "Additional File 1" for the other templates.

Baseline of this outcome is that all approaches excep FET-1k perform similarly well in terms of false-positives.

1.2.2 Outcome O2

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Outcome O2 is presented as Figure X in the original publication.

1.2.3 Outcome On

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Outcome On is presented as Figure X in the original publication.

1.2.4 Further outcomes

If intended, you can add further outcomes here.

1.3 Study design and evidence level

1.3.1 General aspects

• The authors compared four different methods:
  • Gene Set Enrichment Analysis (GSEA)
  • Significance Analysis of Function and Expression (SAFE)
  • sigPathway, and
  • Correlation Adjusted Mean RAnk (CAMERA).
• The authors consider different sizes of the gene sets
• The authors consider different proportions of regulated genes in the gene sets
• The authors consider different magnitudes of the underlying effect size (i.e. log-fold-changes)
• The authors consider three null simulations (without regulation) as reference:
  • permutation of class labels
  • independently sampled expression of all features (=genes)
  • centering the simulated data, i.e. set effect size to zero
• In this publication, the authors published a novel simulation approach termed (FANGS)
• The simulation approach is available in this R package (FANGS) offers the opportunity to reproduce the simulations and repeat the analysis for other gene set methods.
• The authors provide a comprehensive list of the used configuration parameters
• The authors evaluated the following alternative configurations
  • For GSEA one alternative
  • For SAFE five alternative setups
  • For sigPathway and CAMERA no other configurations were considered
• Three experimental data sets were used as foundations for simulating data
  • prostate cancer (264 cases, 160 controls)
  • ischemic stroke (20 cases, 20 controls)
  • normal brain tissue (21 cases, 20 controls)

1.3.2 Design for Outcome O1

• The outcome was generated for ...
• Configuration parameters were chosen ...
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1.3.3 Design for Outcome O2

• The outcome was generated for ...
• Configuration parameters were chosen ...
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1.3.4 Design for Outcome O

• The outcome was generated for ...
• Configuration parameters were chosen ...
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1.5 References

The list of cited or related literature is placed here.