A Bayesian Latent Variable Approach to Aggregation of Partial and Top-Ranked Lists in Genomic Studies

Date

ORCID

Journal Title

Journal ISSN

Volume Title

Publisher

Wiley

item.page.doi

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 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.

Description

Full text access from Treasures at UT Dallas is restricted to current UTD affiliates (use the provided link to the article). Non UTD affiliates will find the web address for this item by clicking the Show full item record link and copying the "relation.uri" metadata.

Keywords

Cluster Analysis, Bayes Theorem, Meta-analysis, Bayesian statistical decision theory, Human genome--Research

item.page.sponsorship

NIH. Grant Number: R15GM113157

Rights

©2018 Wiley

Citation

Collections