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- Title
Nonparametric methods for identifying differentially expressed genes in microarray data.
- Authors
Troyanskaya, Olga G; Garber, Mitchell E; Brown, Patrick O; Botstein, David; Altman, Russ B
- Abstract
Gene expression experiments provide a fast and systematic way to identify disease markers relevant to clinical care. In this study, we address the problem of robust identification of differentially expressed genes from microarray data. Differentially expressed genes, or discriminator genes, are genes with significantly different expression in two user-defined groups of microarray experiments. We compare three model-free approaches: (1). nonparametric t-test, (2). Wilcoxon (or Mann-Whitney) rank sum test, and (3). a heuristic method based on high Pearson correlation to a perfectly differentiating gene ('ideal discriminator method'). We systematically assess the performance of each method based on simulated and biological data under varying noise levels and p-value cutoffs.
- Publication
Bioinformatics (Oxford, England), 2002, Vol 18, Issue 11, p1454
- ISSN
1367-4803
- Publication type
Journal Article
- DOI
10.1093/bioinformatics/18.11.1454