Gene prioritization via weighted Kendall rank aggregation

Gene prioritization is a class of methods for discovering genes implicated in the onset and progression of a disease. As candidate genes are ranked based on similarity to known disease genes according to different set of criteria, the overall aggregation of these ranked datasets is a vital step of the prioritization procedure. Aggregation of different lists of ordered genes is accomplished either via classical order statistics analysis or via combinatorial ordinal data fusion. We propose a novel approach to combinatorial gene prioritization via Linear Programming (LP) optimization and use the recently introduced weighted Kendall τ distance to assess similarities between rankings. The weighted Kendall τ distance allows for constructing aggregates that have higher accuracy at the top of the ranking, usually tested experimentally, and it can also accommodate ties in rankings and handle negative outliers. In addition, the Kendall distance does not use quantitative data which in many instances may be unreliable. We illustrate the performance of the prioritization method on a set of test genes pertaining to the Bardet-Biedl syndrome, schizophrenia, and HIV and show that the combinatorial method matches or outperforms state-of-the art algorithms such as ToppGene.