Weighted Analysis of General Microarray Experiments
Journal article, 2007

Background In DNA microarray experiments, measurements from different biological samples are often assumed to be independent and to have identical variance. For many datasets these assumptions have been shown to be invalid and typically lead to too optimistic p-values. A method called WAME has been proposed where a variance is estimated for each sample and a covariance is estimated for each pair of samples. The current version of WAME is, however, limited to experiments with paired design, e.g. two-channel microarrays. Results The WAME procedure is extended to general microarray experiments, making it capable of handling both one- and two-channel datasets. Two public one-channel datasets are analysed and WAME detects both unequal variances and correlations. WAME is compared to other common methods: fold-change ranking, ordinary linear model with t-tests, LIMMA and weighted LIMMA. The p-value distributions are shown to differ greatly between the examined methods. In a resampling-based simulation study, the p-values generated by WAME are found to be substantially more correct than the alternatives when a relatively small proportion of the genes is regulated. WAME is also shown to have higher power than the other methods. WAME is available as an R-package. Conclusion The WAME procedure is generalized and the limitation to paired-design microarray datasets is removed. The examined other methods produce invalid p-values in many cases, while WAME is shown to produce essentially valid p-values when a relatively small proportion of genes is regulated. WAME is also shown to have higher power than the examined alternative methods.

Author

Anders Sjögren

University of Gothenburg

Chalmers, Mathematical Sciences, Mathematical Statistics

Erik Kristiansson

Chalmers, Mathematical Sciences, Mathematical Statistics

University of Gothenburg

Mats Rudemo

Chalmers, Mathematical Sciences, Mathematical Statistics

University of Gothenburg

Olle Nerman

University of Gothenburg

Chalmers, Mathematical Sciences, Mathematical Statistics

BMC Bioinformatics

1471-2105 (ISSN)

Vol. 8 15-

Subject Categories

Probability Theory and Statistics

DOI

10.1186/1471-2105-8-387

PubMed

17937807

More information

Created

10/7/2017