title: Over-representation of correlation analysis (ORCA): a method for identifying associations between variable sets.
abstract: BACKGROUND
  Often during the analysis of biological data, it is of importance to interpret the correlation structure that exists between variables. Such correlations may reveal patterns of co-regulation that are indicative of biochemical pathways or common mechanisms of response to a related set of treatments. However, analyses of correlations are usually conducted by either subjective interpretation of the univariate covariance matrix or by applying multivariate modeling techniques, which do not take prior biological knowledge into account. Over-representation analysis (ORA) is a simple method for objectively deciding whether a set of variables of known or suspected biological relevance, such as a gene set or pathway, is more prevalent in a set of variables of interest than we expect by chance. However, ORA is usually applied to a set of variables differentiating a single experimental variable and does not take into account correlations.
  
  
  RESULTS
  Over-representation of correlation analysis (ORCA) is a novel combination of ORA and correlation analysis that provides a means to test whether more associations exist between two specific groups of variables than expected by chance. The method is exemplified by application to drug sensitivity and microRNA expression data from a panel of cancer cell lines (NCI60). ORCA highlighted a previously reported correlation between sensitivity to alkylating anticancer agents and topoisomerase inhibitors. We also used this approach to validate microRNA clusters predicted by mRNA correlations. These observations suggest that ORCA has the potential to reveal novel insights from these data, which are not readily apparent using classical ORA.
  
  
  METHODS
  The R code of the method is available at https://github.com/ORCABioinfo/ORCAcode.
authors.affiliation: Department of Surgery and Cancer, Section of Computational and Systems Medicine, Imperial College London, Exhibition Road, London SW7 2AZ, UK and Translational Research Unit, Chulabhorn Research Institute, Bangkok 10210, Thailand Department of Surgery and Cancer, Section of Computational and Systems Medicine, Imperial College London, Exhibition Road, London SW7 2AZ, UK and Translational Research Unit, Chulabhorn Research Institute, Bangkok 10210, Thailand.
authors.forename: Yotsawat
authors.initials: Y
authors.lastname: Pomyen
authors.affiliation: Department of Surgery and Cancer, Section of Computational and Systems Medicine, Imperial College London, Exhibition Road, London SW7 2AZ, UK and Translational Research Unit, Chulabhorn Research Institute, Bangkok 10210, Thailand.
authors.forename: Marcelo
authors.initials: M
authors.lastname: Segura
authors.affiliation: Department of Surgery and Cancer, Section of Computational and Systems Medicine, Imperial College London, Exhibition Road, London SW7 2AZ, UK and Translational Research Unit, Chulabhorn Research Institute, Bangkok 10210, Thailand.
authors.forename: Timothy M D
authors.initials: TM
authors.lastname: Ebbels
authors.affiliation: Department of Surgery and Cancer, Section of Computational and Systems Medicine, Imperial College London, Exhibition Road, London SW7 2AZ, UK and Translational Research Unit, Chulabhorn Research Institute, Bangkok 10210, Thailand.
authors.forename: Hector C
authors.initials: HC
authors.lastname: Keun
mesh_terms: D000477:Alkylating Agents / Q000494:pharmacology; D014408:Biomarkers, Tumor / Q000032:analysis*; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D066264:Datasets as Topic; D004791:Enzyme Inhibitors / Q000494:pharmacology; D020869:Gene Expression Profiling; D023281:Genomics; D006801:Humans; D035683:MicroRNAs / Q000235:genetics*; D058977:Molecular Sequence Annotation / Q000379:methods*; D009369:Neoplasms / Q000188:drug therapy / Q000235:genetics*; D014407:Tumor Cells, Cultured
chemical_list: D000477:Alkylating Agents; D014408:Biomarkers, Tumor; D004791:Enzyme Inhibitors; D035683:MicroRNAs