A general modular framework for gene set enrichment analysis

1 Citation

M Ackermann and K Strimmer, A general modular framework for gene set enrichment analysis, 2009, BMC Bioinformatics, 10:47, pages etc in any possible citation style.

Permanent link to the paper

2 Summary

Gene set analyses have a modular structure, i.e. they consist of

  1. gene level statistics
  2. gene level significance assessment
  3. gene set statistics
  4. gene set significance assessment
  5. statistical conclusion

Alternatively, steps 1.-3. might be replaced by a single global test.

In this paper, 261 different variants of gene set enrichment procedures were evaluated based on simulated and experimental data.

3 Study outcomes

3.1 Outcome O1: Gene level statistics

  • The choice of the gene-level statistics (t, moderated t, or correlation) does NOT have a great impact
  • t statistic, moderated t, and correlation fail to find gene sets that contain up- and downregulated genes

Outcomes O1 and O2 are presented as Table 2 in the original publication.

3.2 Outcome O2: Transformation of the gene level statistics

  • The transformation of the gene level statistic has a substantial impact
  • Transformations help to find gene sets that contain up- and downregulated genes
  • Combination of square transformation and rank transformation shows the best overall performance
  • Binary transformation (i.e. using a cutpoint) and FDRs decrease the performance

Outcomes O1 and O2 are presented as Table 2 in the original publication.

3.3 Outcome O3: Gene set statistics

  • "mean and the maxmean statistic produce ... overall very good results"
  • "median and the Wilcoxon test are primarily advantageous if the competitive null hypothesis is tested, or if there are many outliers in the data"
  • "conditional FDR ... vary strongly with the choice of the gene-level statistic, transformation and permutation approach.
  • The ES score showed a rather weak performance

Outcomes O3 are presented as Table 3 in the original publication.

3.4 Outcome O4: Significance assessment

  • The parametric approach has the best power but is overoptimistic if the assumption of statistical indpendence is violated
  • Permutation seems to slightly outperform resampling
  • "restandardization procedure performs very similar to resampling"

Outcomes O4 are presented as Table 4 in the original publication.

3.5 Outcome O5: Global approaches

  • The performance of the globaltest procedure "is not better than that of the less sophisticated univariate methods" but "is computationally a little bit faster".
  • For Hotellings T2-test:
    • an "overall poor" performance was obtained
    • "the uncorrelated sets are found with the same reliability as with univariate approaches. However, ... the sets with correlation ... are hardly detected."
    • shows "improved performance with sample label permutation as opposed to gene sampling."

Outcomes O5 are presented as Table 5 for the global test and in Table 6 for Hotellings T2 in the original publication.

3.6 Further outcomes

4 Study design and evidence level

4.1 General aspects

  • 100 data sets were simulated
  • The simulated data sets have 600 features (genes) and 20 samples (10 vs. 10)
  • The data was simulated with normally distributed noise with variance equals to one
  • 520 genes were consided as uninformative (delta=0, rho=0)
  • Altogether, nine different simulation data sets were generated that consist of the following combinations:
    • Gene sets with different levels of differential expression (delta \in {0, 0.75, 1, -1}) were simulated
    • Gene sets with varying levels of intra-group correlation (rho \in {0, 0.6, -0.6}) were simulated
    • Gene sets that contain regulated and unregulated genes (half/half) were generated as well as gene set that contain up- and downregulated genes.
  • "The gene set statistic ES was not combined with a binary transformation since the latter does not allow a sensible ranking of the genes."
  • In total
    • 3 gene level statistics ×
    • 5 transformations ×
    • 6 gene set statistics ×
    • 3 significance assessments
    • minus 9 insensible combinations
    • = 261 (in total) variants of gene set analyses were considered
  • The authors count how frequently the p-values that assess significance at the gene-set level are below a significance level 0.05

4.2 Design for Outcome O1: Gene level statistics

  • The authors consider the impact of the selected approach at for module 1 (see summary above)
  • Three approaches were considered: t, moderated t and correlation
  • These approaches were evaluated for five different transformations (see O2)
  • Multiple other approaches
  • The authors already provide the important hint that the dependency on the gene level test statistic might be more relevant for smaller sample size (e.g. 3 vs 3)

4.3 Design for Outcome O2: Transformation of the gene level statistics

  • The outcome was generated for five different transformations (and three gene level statistics)

4.4 Design for Outcome O3: Gene set statistics

  • Three gene set statistics were investigated:
    • mean
    • maxmean
    • median
    • ES
    • conditional FDR
    • Wilcoxon
  • This analyses were performed for the moderated t statistic (gene level) and by using the quadratic transformation. For significance assessment, resampling was applied.

4.5 Design for Outcome O4: Significance assessment

  • Four different approaches for assessing significance at the gene set level were evaluated:
    • parametric
    • resampling
    • permutation
    • restandardization
  • This analysis was performed by using the moderated t as the gene level statistic in combination with a quadratic transformation and the mean as the gene set statistic

4.6 Design for Outcome O5: Global approaches

  • globaltest andHotelling's T2-test with a shrinkage covariance matrix was considered

5 Further comments and aspects

  • Simulation is NOT based on characteristics or gene sets derived from real data
  • The paper provides very comprehensive outcomes in terms of combinations of approaches
  • After the paper was published another type of gene set statistics appeared that is based on Kolmogorov-Smirnov test. This approach is applied e.g. for GSEA.

6 References