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Bioinformatics Advance Access originally published online on February 3, 2007
Bioinformatics 2007 23(8):957-965; 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 1,2, Robert Tibshirani 3,4, Anne-Lise Børresen-Dale 5,6 and Stefanie S. Jeffrey 1,*

1Department of Surgery, Stanford University School of Medicine, 2Department of Mathematics, 3Department of Health, Research & Policy, 4Department of Statistics, Stanford University, 5Department of Genetics, Institute for Cancer Research, Rikshospitalet-Radiumhospitalet Medical Center and 6Medical Faculty, University of Oslo, Oslo, Norway

*To whom correspondence should be addressed.


  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 (ER) 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.

Contact: ssj{at}standford.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Joaquin Dopazo

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

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