Figure 1.
Procedure used to compare 16 gene set analysis methods.
42 microarray datasets were used, each studying a phenotype that has a corresponding KEGG or Metacore disease pathway, that we call target pathway. Each method was applied on each datasets and the p-value and rank of the target pathway in each dataset was used to compare the methods.
Figure 2.
A comparison of sensitivity and prioritization ability of 16 gene set analysis methods.
Each box contains 42 data points representing the p-value (left) and the rank (%) (right) that the target pathway received from a given method when using as input an independent dataset and a collection of gene sets (either KEGG or Metacore). Since the target pathways were designed by KEGG and Metacore for those diseases we expected that, in average, they will be found relevant by the different methods. Methods are ranked from best to worst according to the median p-value (left) and median rank (right).
Figure 3.
False positive rates produced by 16 gene sets analysis methods.
The null hypothesis is simulated by analyzing phenotype permuted versions of the original datasets. The percentage of all pathways found significant at different significance levels (α) is reported for each method with a vertical bar. The horizontal lines denote the expected levels of false positives at each α level. Note the logarithmic scale.
Table 1.
Ranking of gene set analysis methods.
Table 2.
Ranking of gene set analysis methods under several scenarios.