Identifying Differentially Expressed Genes via Weighted Rank Aggregation

Abstract

Identifying differentially expressed genes is an important problem in gene expression analysis, since these genes, exhibiting sufficiently different expression levels under distinct experiment conditions, could be critical for tracing the progression of a disease. In a micro array study, genes are usually sorted in terms of their differentiation abilities with the more differentially expressed genes being ranked higher in the list. As more micro array studies are conducted, rank aggregation becomes an important means to combine such ranked gene lists in order to discover more reliable differentially expressed genes. In this paper, we study a novel weighted gene rank aggregation problem whose complexity is at least NP-hard. To tackle the problem, we develop a new Markov-chain based rank aggregation method called Weighted MC (WMC). The WMC algorithm makes use of rank-based weight information to generate the transition matrix. Extensive experiments on the real biological datasets show that our approach is more efficient in aggregating long gene lists. Importantly, the WMC method is much more robust for identifying biologically significant genes compared with the state-of-the-art methods.