Fig. 4.

Comparison of rank aggregation methods using a compendium of PD datasets. While there is no gold standard for gene expression changes associated with PD, we can judge the aggregation results based on how well the aggregated ranking reproduces the result of an independent study. To simulate this, we have taken a leave-one-out approach where we aggregate all but one of the studies and use the remaining study as a gold standard (top 500 genes are considered positive) to evaluate the aggregation results. We plot the resulting AUCs for different aggregation methods. The horizontal line represents the 99% confidence threshold for AUC being >0.5. We observe that leave-one-out aggregation is predictive for most of the datasets tested, resulting in significant AUCs. We also observe that in most cases BIRRA produces superior results. In particular, it is able to improve our ability to predict blood expression changes (GSE6613) from the remaining datasets that use brain tissue