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Open Access Method

The LeFE algorithm: embracing the complexity of gene expression in the interpretation of microarray data

Gabriel S Eichler12, Mark Reimers13, David Kane14 and John N Weinstein1*

Author Affiliations

1 Genomics and Bioinformatics Groups, Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA

2 Bioinformatics Program, Boston University, Cummington St, Boston, Massachusetts 02215, USA

3 Virginia Commonwealth University, Biostatistics Department, E Marshall St, Richmond, Virginia 23284, USA

4 SRA International, Fair Lakes Court, Fairfax, Virginia 22033, USA

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Genome Biology 2007, 8:R187  doi:10.1186/gb-2007-8-9-r187

Published: 10 September 2007

Additional files

Additional data file 1:

Complete results from the smoker/never-smoker demonstration, including gene categories ranked by LeFE computed median permutation t-test P value and individual gene importance scores.

Format: XLS Size: 777KB Download file

This file can be viewed with: Microsoft Excel Viewer

Open Data

Additional data file 2:

Complete results from the breast cancer demonstration, including gene categories ranked by LeFE computed median permutation t-test P value and individual gene importance scores.

Format: XLS Size: 746KB Download file

This file can be viewed with: Microsoft Excel Viewer

Open Data

Additional data file 3:

Complete results from the gefitinib demonstration, including gene categories ranked by LeFE computed median permutation t-test P value and individual gene importance scores.

Format: XLS Size: 665KB Download file

This file can be viewed with: Microsoft Excel Viewer

Open Data