RankAggreg, an R package for weighted rank aggregation.
BACKGROUND
Researchers 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.


RESULTS
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.


CONCLUSIONS
The 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.
Pihur
Vasyl
V
Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY, USA. v0pihu01@louisville.edu
Datta
Susmita
S
Datta
Somnath
S
D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D012984:Software