Spearman's correlation measures the association between ranked lists. Given a set of ranked lists, we study two tasks: aggregating the set of ranks into one single ranked list, and computing the agreement of the lists as we traverse it. Applications include the analysis of the stability of feature selection and integration of various sources of information. This is illustrated with two examples respectively: We study the stability of identifying variations in GWAS by considering replication studies. In another study, we aggregate genomic distance, 3D associations, and literature information to findpromising disease associated variations. It turns out that these problems can be tackled by considering a multivariate Spearman's correlation.