Bioinformatics Advance Access published online on August 7, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl424
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1 Theoretical Bioinformatics, German Cancer Reseach Center, 69120 Heidelberg, Germany; Medical Research Center, University Hospital Mannheim, 68167 Mannheim, Germany; Cellular and Molecular Pathology, German Cancer Research Center, 69120 Heidelberg, Germany
* To whom correspondence should be addressed.
Motivation: The wide use of DNA microarrays for the investigation of the cell transcriptome triggered the invention of numerous methods for the processing of microarray data and lead to a growing number of microarray studies that examine the same biological conditions. However, comparisons made on the level of gene lists obtained by different statistical methods or from different data sets hardly converge. We aimed at examining such discrepancies on the level of apparently affected biologically related groups of genes, for example metabolic or signalling pathways. This can be achieved by group testing procedures, e.g. over-representation analysis (ORA), bluefunctional class scoring (FCS), or global tests. Results: Three public prostate cancer data sets obtained with the same microarray platform (HGU95A/HGU95av2) were analyzed. Each data set was subjected to normalization by either variance stabilizing normalization (vsn) or mixed model normalization (MMN). Then, statistical analysis of microarrays (SAM) was applied to the vsn-normalized data and mixed model analsis (MMA) to the data normalized by MMN. For multiple testing adjustment the false discovery rate (FDR) was calculated and the threshold was set to 0.05. Gene lists from the same method applied to different data sets showed overlaps between 42% and 52%, while lists from different methods applied to the same data set had between 63% and 85% of genes in common. A number of six gene lists obtained by the two statistical methods applied to the three data sets was then subjected to group testing by blueFisher's exact test. Group testing by GSEA and global test was applied to the three data sets, as well. Fisher's exact test followed by global test showed more consistent results with respect to the concordance between analyses on gene lists obtained by different methods and different data sets than the GSEA. However, all group testing methods identified pathways that had already been described to be involved in the pathogenesis of prostate cancer. Moreover, pathways recurrently identified in these analyses are more likely to be reliable than those from a single analysis on a single data set. Supplementary Info: Supplementary Figure 1 and Supplementary Tables 1-4 are available from the Journal's website.
Received March 8, 2006
Revised July 22, 2006
Accepted July 28, 2006
Article
Group testing for pathway analysis improves comparability of different microarray data sets
Theodora Manoli 1, Norbert Gretz 2, Hermann-Josef Gröne 3, Marc Kenzelmann 3, Roland Eils 4, and Benedikt Brors 4 *
2 Medical Research Center, University Hospital Mannheim, 68167 Mannheim, Germany
3 Cellular and Molecular Pathology, German Cancer Research Center, 69120 Heidelberg, Germany
4 Theoretical Bioinformatics, German Cancer Reseach Center, 69120 Heidelberg, Germany
Benedikt Brors, E-mail: b.brors{at}dkfz.de
Abstract
Associate Editor: David Rocke
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