Figure 2

Sensitivity analysis of parameter NoG for DIGS model with SMO (A) and NN (B) classifiers. For each of the 8 datasets, the proposed DIGS model is applied to infer pathway activity while setting NoG, i.e. the maximum number of member genes in a pathway allowed to have non-zero weights, to 5, 10, 15 and 20. In addition, DIGS model is also applied with NoG set to equal to the number of available member genes in a pathway, i.e. all member genes can take non-zero weights to construct pathway activity. A classifier is trained using the pathway activity profiles and tests the prediction accuracy. For both SMO (A) and NN (B) classifiers, it is clear that the proposed DIGS model is robust to the parameter NoG during the tested ranged 5 to 20. Furthermore, constraining the maximum number of active constituent genes appears to generally improve classification accuracy as DIGS_ALL usually leads to lower prediction rate compared with the others.