Open Access Highly Accessed Methodology article

Improving gene set analysis of microarray data by SAM-GS

Irina Dinu1, John D Potter2, Thomas Mueller3, Qi Liu1, Adeniyi J Adewale1, Gian S Jhangri1, Gunilla Einecke3, Konrad S Famulski3, Philip Halloran3 and Yutaka Yasui1*

Author Affiliations

1 Department of Public Health Sciences, School of Public Health, University of Alberta, Edmonton, Alberta, T6G 2G3, Canada

2 Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, 98109-1024, USA

3 Division of Nephrology & Transplantation Immunology, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, T6G 2S2, Canada

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BMC Bioinformatics 2007, 8:242  doi:10.1186/1471-2105-8-242

Published: 5 July 2007

Additional files

Additional file 1:

Gene-set simulation experiment results with the sex, p53, and leukemia datasets. The results of the gene-set simulation experiments using the three datasets are given.

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Additional file 2:

Histogram of Pearson correlation with the phenotype in the mouse-microarray kidney-transplant study. Histogram of Pearson correlation with the phenotype for 16,612 individual genes in the mouse-microarray kidney-transplant study.

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