title: Robust rank aggregation for gene list integration and meta-analysis.
abstract: BACKGROUND
  The continued progress in developing technological platforms, availability of many published experimental datasets, as well as different statistical methods to analyze those data have allowed approaching the same research question using various methods simultaneously. To get the best out of all these alternatives, we need to integrate their results in an unbiased manner. Prioritized gene lists are a common result presentation method in genomic data analysis applications. Thus, the rank aggregation methods can become a useful and general solution for the integration task.
  
  
  RESULTS
  Standard rank aggregation methods are often ill-suited for biological settings where the gene lists are inherently noisy. As a remedy, we propose a novel robust rank aggregation (RRA) method. Our method detects genes that are ranked consistently better than expected under null hypothesis of uncorrelated inputs and assigns a significance score for each gene. The underlying probabilistic model makes the algorithm parameter free and robust to outliers, noise and errors. Significance scores also provide a rigorous way to keep only the statistically relevant genes in the final list. These properties make our approach robust and compelling for many settings.
  
  
  BACKGROUND
  All the methods are implemented as a GNU R package RobustRankAggreg, freely available at the Comprehensive R Archive Network http://cran.r-project.org/.
authors.affiliation: Institute of Computer Science, University of Tartu, Liivi 2- 314, 50409 Tartu, Estonia.
authors.forename: Raivo
authors.initials: R
authors.lastname: Kolde
authors.forename: Sven
authors.initials: S
authors.lastname: Laur
authors.forename: Priit
authors.initials: P
authors.lastname: Adler
authors.forename: Jaak
authors.initials: J
authors.lastname: Vilo
mesh_terms: D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D055786:Gene Knockout Techniques; D023281:Genomics; D006801:Humans; D015201:Meta-Analysis as Topic; D051379:Mice; D013234:Stem Cells / Q000378:metabolism; D015003:Yeasts / Q000235:genetics