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Table 2 Comparison of the properties of several geneset differential expression analysis methods

From: Functional Analysis: Evaluation of Response Intensities - Tailoring ANOVA for Lists of Expression Subsets

Author Year Hypothesis Data used Group statistic Significance Name Properties Individual statistic
Mootha et al 2003 Competitive Individual statistic ES (Running Sum) Sample permutations GSEA Hybrid Signal/noise Ratio
Subramanian et al 2005 Competitive Individual statistic ES (Running Sum) Sample permutations GSEA Hybrid, asymmetrical Individual correlation (r)
Keller et al 2007 Competitive Individual statistic ES (Running Sum) Competitive theoretical model Variant GSEA Hybrid, symmetrical *
Effron & Tibshirani 2007 Competitive Individual statistic ES (Running Sum) Sample permutations GSA Restandardizaton *
Pavlidis et al 2004 Competitive Individual statistic Log (p(g)) = mean[log[p(i)]] Genes permutations   Depends on the geneset size Pearson correlation coef.
Tian et al 2005 Competitive Individual statistic Weighted mean Genes permutations   Standardization Student t
Tian et al 2005 Self-contained Individual statistic Weighted mean Sample permutations   Standardization Student t
Kim & Volsky 2005 Self-contained Individual statistic Mean Normal distribution PAGE Central Limit Theorem Fold-change
Effron & Tibshirani 2007 Self-contained Individual statistic Mean Sample permutations GSA Unidirectional
+ Restandardization
Student t
Effron & Tibshirani 2007 Self-contained Individual statistic Maxmean Sample permutations GSA Unidirectional (directional subset) + Restandardization Student t
Effron & Tibshirani 2007 Self-contained Individual statistic Absmean Sample permutations GSA Unidirectional (absolute value) + Restandardization Student t
Dinu et al 2007 Self-contained Expression data Sum (d2) Sample permutations SAM-GS Determination of S0 SAM d statistic
Goeman et al 2004 Self-contained Expression data Q(g) = mean(Q(i)) Permutation/Gamma/Asymptotic GlobalTest P (Y|X) Q(i)
Mansmann & Meister 2005 Self-contained Expression data F Sample permutations GlobalAncova P (X|Y)  
Berger et al Unpublished Self-contained Expression data F Fisher F ANOVA-2 Unidirectional  
Berger et al Unpublished Self-contained Expression data F* Sample permutations/random data FAERI Bidirectional  
  1. The methods are grouped into categories. The upper and lower parts of the table list respectively the competitive and self-contained methods. The methods highlighted in bold rely on a two-step procedure. The methods in plain writing rely on a global analysis, which uses the expression data to compute the geneset statistic in one single step, based on multivariate models. Finally, ANOVA-2 and FAERI are shown in italics.