Indirect two-sided relative ranking: a robust similarity measure for gene expression data

Background: There is a large amount of gene expression data that exists in the public domain. This data has been generated under a variety of experimental conditions. Unfortunately, these experimental variations have generally prevented researchers from accurately comparing and combining this wealth of data, which still hides many novel insights. Results: In this paper we present a new method, which we refer to as indirect two-sided relative ranking, for comparing gene expression profiles that is robust to variations in experimental conditions. This method extends the current best approach, which is based on comparing the correlations of the up and down regulated genes, by introducing a comparison based on the correlations in rankings across the entire database. Because our method is robust to experimental variations, it allows a greater variety of gene expression data to be combined, which, as we show, leads to richer scientific discoveries. Conclusions: We demonstrate the benefit of...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Louis Licamele, Lise Getoor
• Keywords: BMCBI 2010, Gene Expression, Gene Expression Data, Indirect Method