# How to calculate AUC for any correlation method?

I want to know how to calculate AUC to compare correlation methods.

Is there any idea how the authors of above paper have calculated AUC for each method?

UPDATE: Here is the Full Paper

According to authors;

To calculate the area under the ROC curve, we computed the Riemman sum with intervals of 0.001.

Table 1 describes the areas under ROC curves

• AUC is used for methods that enable prediction, while correlation describes strength of relation between two variables, so it is not really clear what you are asking..? The paper you refer to is not available in open access, so we are not possible to refer to it for additional hints. – Tim May 8 '15 at 15:48
• I have updated the question. I think that authors have calculated AUC. – statuser May 9 '15 at 19:33
• After reading the paper my guess is that they used $p$-values for those measures as a cutoff as a prediction criteria for existence of a relation, and then used a classical ROC/AUC for binary classifiers. – Tim May 11 '15 at 9:30
In a contrived sense, the AUC is equivalent to another association measure: the rank based U-statistic. This measures correlation in a probabilistic sense; it is for any two randomly sampled observations, $(X_1, Y_1)$ and $(X_2, Y_2)$ the probability $P(X_2 > X_1 | Y_2 > Y_1)$. However, the U-statistic is a weak measure of correlation at best. It's not really appropriate for genomics where actually modeling the exact functional form of the relationship is often more important.