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Over-representation of correlation analysis (ORCA): a method for identifying associations between variable sets.

Yotsawat Pomyen1, Marcelo Segura2, Timothy M D Ebbels2

  • 1Department 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.

Bioinformatics (Oxford, England)
|September 4, 2014
PubMed
Summary
This summary is machine-generated.

Over-representation of correlation analysis (ORCA) combines correlation and over-representation analysis (ORA) to find significant associations between biological variable groups. This novel method reveals insights not apparent with classical ORA, as demonstrated with cancer cell line data.

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Interpreting correlation structures in biological data is crucial for understanding co-regulation and biological pathways.
  • Traditional methods like univariate covariance matrix analysis or multivariate modeling often neglect prior biological knowledge.
  • Over-representation analysis (ORA) objectively identifies biological relevance but typically ignores correlations between variables.

Purpose of the Study:

  • To introduce Over-representation of Correlation Analysis (ORCA), a novel method integrating ORA with correlation analysis.
  • To provide a computational tool for testing the significance of associations between two specific groups of biological variables.
  • To uncover novel biological insights from complex datasets by considering both correlation and biological relevance.

Main Methods:

  • ORCA combines principles of ORA and correlation analysis to assess if observed associations between variable groups exceed chance expectations.
  • The method was applied to analyze drug sensitivity and microRNA expression data from the NCI60 cancer cell line panel.
  • The R code for ORCA is publicly available, facilitating its application and further development.

Main Results:

  • ORCA successfully identified a known correlation between sensitivity to alkylating anticancer agents and topoisomerase inhibitors in cancer cell lines.
  • The method was utilized to validate microRNA clusters predicted through mRNA correlations.
  • These findings demonstrate ORCA's capability to reveal significant biological associations that may be missed by conventional approaches.

Conclusions:

  • ORCA offers a powerful new approach for analyzing biological data by integrating correlation and over-representation analysis.
  • The method has the potential to uncover previously unrecognized relationships and biological insights from complex datasets.
  • ORCA's application to cancer cell line data highlights its utility in drug sensitivity and molecular expression studies.