PenDA, a rank-based method for personalized differential analysis: Application to lung cancer
Fig 2
Parameter analysis and predictive power.
ROC curves (true positive rate TPR vs false positive rate FPR) of the PenDA method on simulated datasets. The curves were obtained by varying the proportion threshold h for various values of other method parameters or of properties of the investigated dataset. Insets show the maximal informedness that represent the maximal value of the difference TPR-FPR computed for each ROC curve. (a) Effect of the maximal size l of L and H lists. (b) Impact of the number of control samples used to infer the L and H lists. (c) Effect of the total number of genes in the dataset. (d) Impact of the proportion of deregulated genes in the tumorous samples.