Rank aggregation for candidate gene identification

Abstract

Differences of molecular processes are reflected, among others, by differences in gene expression levels of the involved cells. High-throughput methods such as microarrays and deep sequencing approaches are increasingly used to obtain these expression profiles. Often differences of gene expression across different conditions such as tumor vs inflammation are investigated. Top scoring differential genes are considered as candidates for further analysis. Measured differences may not be related to a biological process as they can also be caused by variation in measurement or by other sources of noise. A method for reducing the influence of noise is to combine the available samples. Here, we analyze different types of combination methods, early and late aggregation and compare these statistical and positional rank aggregation methods in a simulation study and by experiments on real microarray data.