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Integrating diverse genomic data using gene sets

Svitlana Tyekucheva12, Luigi Marchionni3, Rachel Karchin4 and Giovanni Parmigiani12*

Author Affiliations

1 Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02115, USA

2 Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA

3 Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, 1550 Orleans Street, Baltimore, MD 21231, USA

4 Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA

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Genome Biology 2011, 12:R105  doi:10.1186/gb-2011-12-10-r105

Published: 21 October 2011

Abstract

We introduce and evaluate data analysis methods to interpret simultaneous measurement of multiple genomic features made on the same biological samples. Our tools use gene sets to provide an interpretable common scale for diverse genomic information. We show we can detect genetic effects, although they may act through different mechanisms in different samples, and show we can discover and validate important disease-related gene sets that would not be discovered by analyzing each data type individually.