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Bioinformatics Vol. 19 no. 10 2003
Pages 1284-1289
© 2003 Oxford University Press

A comparison of parametric versus permutation methods with applications to general and temporal microarray gene expression data

Ronghui Xu * and Xiaochun Li

Department of Biostatistics, Harvard School of Public Health and Dana-Farber Cancer Institute, 44 Binney Street, Boston, MA 02115, USA

Received on October 22, 2002 ; revised on January 21, 2003 ; accepted on January 25, 2003

Motivation: In analyses of microarray data with a design of different biological conditions, ranking genes by their differential ‘importance’ is often desired so that biologists can focus research on a small subset of genes that are most likely related to the experiment conditions. Permutation methods are often recommended and used, in place of their parametric counterparts, due to the small sample sizes of microarray experiments and possible non-normality of the data. The recommendations, however, are based on classical knowledge in the hypothesis test setting.

Results: We explore the relationship between hypothesis testing and gene ranking. We indicate that the permutation method does not provide a metric for the distance between two underlying distributions. In our simulation studies permutation methods tend to be equally or less accurate than parametric methods in ranking genes. This is partially due to the discreteness of the permutation distributions, as well as the non-metric property. In data analysis the variability in ranking genes can be assessed by bootstrap. It turns out that the variability is much lower for permutation than parametric methods, which agrees with the known robustness of permutation methods to individual outliers in the data.

Contact: rxu{at}jimmy.harvard.edu

* To whom correspondence should be addressed.


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