A Bayesian latent variable approach to aggregation of partial and top‐ranked lists in genomic studies

In genomic research, it is becoming increasingly popular to perform meta‐analysis, the practice of combining results from multiple studies that target a common essential biological problem. Rank aggregation, a robust meta‐analytic approach, consolidates such studies at the rank level. There exists extensive research on this topic, and various methods have been developed in the past. However, these methods have two major limitations when they are applied in the genomic context. First, they are mainly designed to work with full lists, whereas partial and/or top‐ranked lists prevail in genomic studies. Second, the component studies are often clustered, and the existing methods fail to utilize such information. To address the above concerns, a Bayesian latent variable approach, called BiG, is proposed to formally deal with partial and top‐ranked lists and incorporate the effect of clustering. Various reasonable prior specifications for variance parameters in hierarchical models are carefully studied and compared. Simulation results demonstrate the superior performance of BiG compared with other popular rank aggregation methods under various practical settings. A non–small‐cell lung cancer data example is analyzed for illustration.