  ResearchA weighted average difference method for detecting differentially expressed genes from microarray dataKoji Kadota Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan
Algorithms for Molecular Biology 2008, 3:8doi:10.1186/1748-7188-3-8
Additional filesAdditional file 1: Detailed information for Datasets 3–38. Format: DOC Size: 2.4MB Download file This file can be viewed with: Microsoft Word Viewer Additional file 4: Average expression vectors and the results of outlier detection for Datasets 3–26. Sheet 1: Average expression vectors are provided. Sheet 2: For each of the original average expression vectors, an outlier vector (consisting of 1 for over-expressed outliers, -1 for under-expressed outliers, and 0 for non-outliers) is provided. This sheet does not contain "-1". Format: XLS Size: 14.4MB Download file This file can be viewed with: Microsoft Excel Viewer Additional file 5: Average expression vectors and the results of outlier detection for Datasets 27–38. Sheet 1: Average expression vectors are provided. Sheet 2: For each of the original average expression vectors, an outlier vector (consisting of 1 for over-expressed outliers, -1 for under-expressed outliers, and 0 for non-outliers) is provided. This sheet does not contain "-1". Format: XLS Size: 8.2MB Download file This file can be viewed with: Microsoft Excel Viewer |
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