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Analyses of high-throughput genomic data often lead to ranked lists ofgenomic loci. How to characterize concordant signals between two rank lists isa common problem with many applications. One example is measuring thereproducibility between two replicate experiments. Another is to characterizethe interaction and co-binding between two transcription factors (TF) based onthe overlap between their binding sites. As an exploratory tool, the simpleVenn diagram approach can be used to show the common loci between two lists.However, this approach does not account for changes in overlap with decreasingranks, which may contain useful information for studying similarities ordissimilarities of the two lists. The recently proposed irreproduciblediscovery rate (IDR) approach compares two rank lists using a copula mixturemodel. This model considers the rank correlation between two lists. However, itonly analyzes the genomic loci that appear in both lists, thereby onlymeasuring signal concordance in the overlapping set of the two lists. When twolists have little overlap but loci in their overlapping set have highconcordance in terms of rank, the original IDR approach may misleadingly claimthat the two rank lists are highly reproducible when they are indeed not. Inthis article, we propose to address the various issues above by translating theproblem into a bivariate survival problem. A survival copula mixture model isdeveloped to characterize concordant signals in two rank lists. Theeffectiveness of this approach is demonstrated using both simulations and realdata.

A Survival Copula Mixture Model for Comparing Two Genomic Rank Lists