Methodology article
Microarray-based gene set analysis: a comparison of current methods
-
* Corresponding author: Michael A Black mik.black@otago.ac.nz
1 Department of Statistics, University of Auckland, Auckland, New Zealand
2 Department of Biochemistry, University of Otago, Dunedin, New Zealand
BMC Bioinformatics 2008, 9:502 doi:10.1186/1471-2105-9-502
Published: 27 November 2008Additional files
Additional file 1:
Detection rates for the six gene set analysis methods on simple simulated data. In contrast to Table 2, all altered gene sets were simulated so as to exhibit changes in the same direction. This resulted in a major performance improvement for the sigPathway approach. 100 data sets (each containing 20 genes in 20 sets) were analyzed by each method, with 10,000 permutations used to generate p-values to which FDR controlling adjustments [21] were made. An adjusted p-value of 0.05 was required for significance. The value in each cell relates to the proportion of each type of gene set activity correctly identified by each method. Standard errors are shown in parentheses.
Format: PDF Size: 23KB Download file
This file can be viewed with: Adobe Acrobat Reader
Additional file 2:
The distribution of difference and pairwise correlations in diabetes data[3]. (a) difference for each gene, positive value indicates up-regulation in DM2 samples (b) average difference for each gene set (c) pairwise correlations for all gene pairs using DM2 samples (d) average pairwise correlations for each gene set using DM2 samples (e) pairwise correlations for all pairs of genes using NGT samples (f) average pairwise correlations for each gene set using NGT samples.
Format: JPEG Size: 204KB Download file
Additional file 3:
The distribution of difference and pairwise correlations in the leukemia data[22]. (a) difference for each gene, positive value indicates up-regulation in AML samples (b) average difference for each gene set (c) pairwise correlations for all gene pairs using AML samples (d) average pairwise correlations for each gene set using AML samples (e) pairwise correlations for all pairs of genes using ALL samples (f) average pairwise correlations for each gene set using ALL samples.
Format: JPEG Size: 197KB Download file
Additional file 4:
Application of gene set analysis methods to diabetes data[3](all gene sets). Ranked (by p-value) gene sets produced by each of the six analysis methods. NP indicates the nominal p-values and AP indicates the FDR adjusted p-values.
Format: PDF Size: 74KB Download file
This file can be viewed with: Adobe Acrobat Reader
Additional file 5:
Application of gene set analysis methods to leukemia data[22](all gene sets). Ranked (by p-value) gene sets produced by each of the six analysis methods. NP indicates the nominal p-values and AP indicates the FDR adjusted p-values.
Format: PDF Size: 65KB Download file
This file can be viewed with: Adobe Acrobat Reader