Difference between revisions of "Gene set analysis methods: a systematic comparison"

(First version)
 
(→‎Study design and evidence level)
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=== Study design and evidence level ===
 
=== Study design and evidence level ===
 
==== General aspects ====
 
==== General aspects ====
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* In this publication, the authors published a novel simulation approach termed (FANGS)
 
 
* The authors compared four different methods:  
 
* The authors compared four different methods:  
 
** Gene Set Enrichment Analysis (GSEA)
 
** Gene Set Enrichment Analysis (GSEA)
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** sigPathway, and  
 
** sigPathway, and  
 
** Correlation Adjusted Mean RAnk (CAMERA).
 
** 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)
  
 
==== Design for Outcome O1 ====
 
==== Design for Outcome O1 ====

Revision as of 10:00, 25 February 2020

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.

Permanent link to the paper


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

The performance of ...

Outcome O1 is presented as Figure X in the original publication.

1.2.2 Outcome O2

...

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

1.2.3 Outcome On

...

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 ...
  • ...

1.3.3 Design for Outcome O2

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

...

1.3.4 Design for Outcome O

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

1.4 Further comments and aspects

1.5 References

The list of cited or related literature is placed here.

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