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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

Additional files

Additional file 1:

Tables with additional results for TCGA GBM data. Table S1: P-values for the top 50 gene sets discovered by the integrative method (INT) using the competitive gene set test. Patients from the upper tertile of the survival distribution were labeled as long-term survivors, and those from the lower tertile short-term survivors. Table S2: P-values for the top 50 gene sets discovered by the integrative method (INT) using the competitive gene set test. A Cox regression model was used to establish gene-to-phenotype association scores.

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Open Data

Additional file 2:

R code for methods described in the paper. An archive containing the R implementation of the methods described in the paper, and used for simulations and data analysis.

Format: GZ Size: 235KB Download file

Open Data