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

ABSTRACT: 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

SUBMITTER: Li X 

PROVIDER: S-EPMC6235700 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature