The multi-reference contrast method: Facilitating set enrichment analysis

Authors:
CristóBal Fresno;Andrea S. Llera;MaríA R. Girotti;MaríA P. Valacco;Juan A. LóPez;Osvaldo L. Podhajcer;MóNica G. Balzarini;Federico Prada;Elmer A. FernáNdez
Affiliations:
BioScience Data Mining Group, Catholic University of Córdoba, Córdoba, Argentina;CONICET, Buenos Aires, Argentina and Laboratory of Molecular and Cellular Therapy, Leloir Institute, Buenos Aires, Argentina;CONICET, Buenos Aires, Argentina and Laboratory of Molecular and Cellular Therapy, Leloir Institute, Buenos Aires, Argentina;CONICET, Buenos Aires, Argentina and Laboratory of Molecular and Cellular Therapy, Leloir Institute, Buenos Aires, Argentina;National Center for Cardiovascular Research, Madrid, Spain;CONICET, Buenos Aires, Argentina and Laboratory of Molecular and Cellular Therapy, Leloir Institute, Buenos Aires, Argentina;CONICET, Buenos Aires, Argentina and Biometry Laboratory, National University of Córdoba, Córdoba, Argentina;Institute of Technology, School of Engineering and Sciences, UADE, Buenos Aires, Argentina;BioScience Data Mining Group, Catholic University of Córdoba, Córdoba, Argentina and CONICET, Buenos Aires, Argentina
Venue:
Computers in Biology and Medicine
Year:
2012

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Abstract

Set enrichment analysis (SEA) is used to identify enriched biological categories/terms within high-throughput differential expression experiments. This is done by evaluating the proportion of differentially expressed genes against a background reference (BR). However, the choice of the ''appropriate'' BR is a perplexing problem and results will depend on it. Here, a visualization procedure that integrates results from several BRs and a stability analysis of enriched terms is presented as a tool to aid SEA. The multi-reference contrast method (MRCM) combines results from multiple BRs in a unique picture. The application of the proposed method was illustrated in one proteomic and three microarray experiments. The MRCM facilitates the exploration task involved in ontology analysis on proteomic/genomic experiments, where consensus terms were found to validate main experimental hypothesis. The use of more than one reference may provide new biological insights. The tool automatically highlights non-consensus terms assisting SEA.