A practical false discovery rate approach to identifying patterns of differential expression in microarray data
- 29 March 2005
- journal article
- research article
- Published by Oxford University Press (OUP) in Bioinformatics
- Vol. 21 (11) , 2684-2690
- https://doi.org/10.1093/bioinformatics/bti407
Abstract
Searching for differentially expressed genes is one of the most common applications for microarrays, yet statistically there are difficult hurdles to achieving adequate rigor and practicality. False discovery rate (FDR) approaches have become relatively standard; however, how to define and control the FDR has been hotly debated. Permutation estimation approaches such as SAM and PaGE can be effective; however, they leave much room for improvement. We pursue the permutation estimation method and describe a convenient definition for the FDR that can be estimated in a straightforward manner. We then discuss issues regarding the choice of statistic and data transformation. It is impossible to optimize the power of any statistic for thousands of genes simultaneously, and we look at the practical consequences of this. For example, the log transform can both help and hurt at the same time, depending on the gene. We examine issues surrounding the SAM 'fudge factor' parameter, and how to handle these issues by optimizing with respect to power.Keywords
This publication has 7 references indexed in Scilit:
- Improving false discovery rate estimationBioinformatics, 2004
- On the use of permutation in and the performance of a class of nonparametric methods to detect differential gene expressionBioinformatics, 2003
- Empirical bayes methods and false discovery rates for microarraysGenetic Epidemiology, 2002
- The control of the false discovery rate in multiple testing under dependencyThe Annals of Statistics, 2001
- Significance analysis of microarrays applied to the ionizing radiation responseProceedings of the National Academy of Sciences, 2001
- Generation of patterns from gene expression data by assigning confidence to differentially expressed genesBioinformatics, 2000
- Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple TestingJournal of the Royal Statistical Society Series B: Statistical Methodology, 1995