Figure 3.

Comparison of CMI and other inference methods-based COMBINER using LDA-CFE classifiers focused on the top 100 inferred pathways. Seven methods were compared here, including CMI, CORG, Mean, Median, PCA, LLR and Individual Gene. (a) Classification accuracy for best feature set: pair-wise comparisons. Starting from all 100 inferred pathway activities, we recursively removed the activity with the lowest average weight from 500 LDA classifiers, until the maximum average AUC was reached. The process was repeated 100 times and the most frequently occurring marker set was regarded as the ultimate marker. We measured classification accuracy of each method by computing AUC mean ± standard error for the final feature set. (b) Classification accuracy overall. The overall classification accuracy was measured by computing the average maximum mean AUC of all six inference-validation pairs. On average, CMI was superior to the other methods, even though its activity vector consisted of expression values from only a few genes in each pathway.

Yang et al. BMC Bioinformatics 2012 13:12   doi:10.1186/1471-2105-13-12
Download authors' original image