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Bioinformatics Advance Access published online on February 3, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm033
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Disease–Specific Genomic Analysis: Identifying the Signature of Pathologic Biology

Monica Nicolau a,b, Robert Tibshirani c,d, Anne-Lise Børresen-Dale e,f and Stefanie S. Jeffrey a,*

aDepartment of Surgery, Stanford University School of Medicine, b Department of Mathematics, cDepartment of Health, Research & Policy, and dDepartment of Statistics, Stanford University, eDepartment of Genetics, Institute for Cancer Research, Rikshospitalet-Radiumhospitalet Medical Center, Oslo, Norway, fMedical Faculty, University of Oslo, Norway.

*To whom correspondence should be addressed. Dr. Stefanie Jeffrey, E-mail: ssj{at}stanford.edu


  Abstract

Motivation: Genomic high-throughput technology generates massive data, providing opportunities to understand countless facets of the functioning genome. It also raises profound issues in identifying data relevant to the biology being studied.

Results: We introduce a method for the analysis of pathologic biology that unravels the disease characteristics of high dimensional data. The method, Disease-Specific Genomic Analysis (DSGA), is intended to precede standard techniques like clustering or class prediction, and enhance their performance and ability to detect disease. DSGA measures the extent to which the disease deviates from a continuous range of normal phenotypes, and isolates the aberrant component of data. In several microarray cancer datasets, we show that DSGA outperforms standard methods. We then use DSGA to highlight a novel subdivision of an important class of genes in breast cancer, the Estrogen Receptor cluster. We also identify new markers distinguishing ductal and lobular breast cancers. Although our examples focus on microarrays, DSGA generalizes to any high dimensional genomic/proteomic data.

Associate Editor: Dr. Joaquin Dopazo

Received on May 9, 2006; revised on December 22, 2006; accepted on January 28, 2007

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