Figure 1.
Coloration from green to red represent differential expression levels, where dark green corresponds to high over expression and dark red indicates severe under expression. Edges with arrows and bars represent catalytic and inhibitory relationships, respectively. Considering A1..A8 as one set results in inconclusive patterns of gene expression. By considering pathway relationships, A3A4A7 is recognized as a path of differentially expressed genes.
Table 1.
Term definitions.
Figure 2.
A visual representation of the DEAP algorithm workflow described in Methods.
Figure 3.
Five pathways designed to test analysis approaches are illustrated. Nodes labelled in green and red were built around distributions with μ of +X and −X, respectively, where X represents a numerical value. Gray nodes represent data sampled from the standard normal distribution. Edges with arrows and bars represent catalytic and inhibitory relationships, respectively.
Figure 4.
Power curve, variable pathway effect.
Performance of GSEA, SPIA, and DEAP are compared as pathway effect (μ) changes. Specific values are indicated at μ = 1. Power (y-axis) is the ratio of simulations, out of 5000 (5 pathways, 1000 simulations each), which were identified as significant (p<0.05). Constants were σ2g = 0 and sample size = 10.
Figure 5.
Power curve, variable gene variance.
Performance of GSEA, SPIA, and DEAP are compared as gene variance (σ2g) changes. Power (y-axis) is the ratio of simulations, out of 5000 (5 pathways, 1000 simulations each), which were identified as significant (p<0.05). Constants were μ = 0.5 and sample size = 10.
Figure 6.
Power curve, variable sample size.
Performance of GSEA, SPIA, and DEAP are compared as sample size changes. Power (y-axis) is the ratio of simulations, out of 5000 (5 pathways, 1000 simulations each), which were identified as significant (p<0.05). Constants were σ2g = 0 and μ = 0.5.
Figure 7.
Type I error.
Figure 8.
Simulated data on the TGFβ signalling pathway, power vs. pathway effect, variance, and sample size.
At the top, the KEGG TGFβ signaling pathway is illustrated, with green, red, and grey nodes representing nodes whose simulated values were +μ, −μ, and 0, respectively [13]. The nodes are colored to indicate activity leading to G1 arrest in the cell cycle. At the bottom, power for detecting significant differential expression in this pathway is illustrated with respect to pathway effect, variance, and sample size. Figure adapted from http://www.genome.jp/kegg-bin/show_pathway?map04350 with permission from KEGG.
Figure 9.
Simulated data on the post-transcriptional silencing by small RNAs, power vs. pathway effect, variance, and sample size.
At the top, the Reactome post-transcriptional silencing by small RNAs pathway is illustrated, with green, red, and grey nodes representing nodes whose simulated values were +μ, −μ, and 0, respectively [14]. The nodes are colored to indicate activity leading to silencing by cleaved RNA with 5′ phosphate and 3′ hydroxyl. At the bottom, power for detecting significant differential expression in this pathway is illustrated with respect to pathway effect, variance, and sample size.
Table 2.
Results from Interferon microarray data analysis using GSEA, SPIA, and DEAP [36], [37].
Figure 10.
Maximally differentially expressed path identification.
Maximally differentially expressed path identification by DEAP on the Notch signalling pathway. Pathway image is from PANTHER [11], [46]. The path shaded in purple was identified by DEAP as the most differentially expressed. Numerical values are log-expression ratios from the Interferon microarray study [36], [37].
Table 3.
Results from the COPD proteomics data analysis using GSEA, SPIA, and DEAP (Methods: Biological data).