Method
The LeFE algorithm: embracing the complexity of gene expression in the interpretation of microarray data
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* Corresponding author: John N Weinstein weinstein@dtpax2.ncifcrf.gov
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
Genome Biology 2007, 8:R187 doi:10.1186/gb-2007-8-9-r187
Published: 10 September 2007Additional 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
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
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