Abstract
BackgroundResearchers in the field of bioinformatics often face a challenge of combining several ordered lists in a proper and efficient manner. Rank aggregation techniques offer a general and flexible framework that allows one to objectively perform the necessary aggregation. With the rapid growth of high-throughput genomic and proteomic studies, the potential utility of rank aggregation in the context of meta-analysis becomes even more apparent. One of the major strengths of rank-based aggregation is the ability to combine lists coming from different sources and platforms, for example different microarray chips, which may or may not be directly comparable otherwise.ResultsThe RankAggreg package provides two methods for combining the ordered lists: the Cross-Entropy method and the Genetic Algorithm. Two examples of rank aggregation using the package are given in the manuscript: one in the context of clustering based on gene expression, and the other one in the context of meta-analysis of prostate cancer microarray experiments.ConclusionThe two examples described in the manuscript clearly show the utility of the RankAggreg package in the current bioinformatics context where ordered lists are routinely produced as a result of modern high-throughput technologies.
Highlights
Researchers in the field of bioinformatics often face a challenge of combining several ordered lists in a proper and efficient manner
We illustrate our R package with two different rank aggregation problems, one in the context of unsupervised learning where there is an intrinsic difficulty of choosing the best clustering algorithm for a particular problem, and another one in the context of metaanalysis of several microarray cancer studies where the goal is to determine the combined set of genes indicative of the cancer status
Rank aggregation is helpful in reconciling the ranks and producing the "super"-list which determines the overall winner and ranks all clustering algorithms based on their performance as determined by all m validation measures simultaneously
Summary
The RankAggreg package provides two methods for combining the ordered lists: the Cross-Entropy method and the Genetic Algorithm. Two examples of rank aggregation using the package are given in the manuscript: one in the context of clustering based on gene expression, and the other one in the context of meta-analysis of prostate cancer microarray experiments
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