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Weighted Analysis of General Microarray Experiments

Anders Sjögren (Institutionen för matematiska vetenskaper, matematisk statistik) ; Erik Kristiansson (Institutionen för matematiska vetenskaper, matematisk statistik) ; Mats Rudemo (Institutionen för matematiska vetenskaper, matematisk statistik) ; Olle Nerman (Institutionen för matematiska vetenskaper, matematisk statistik)
BMC Bioinformatics (1471-2105). Vol. 8 (2007), p. 15.
[Artikel, refereegranskad vetenskaplig]

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.


This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.



Denna post skapades 2007-12-17. Senast ändrad 2014-01-29.
CPL Pubid: 63266

 

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Institutioner (Chalmers)

Institutionen för matematiska vetenskaper, matematisk statistik (2005-2016)

Ämnesområden

Matematisk statistik

Chalmers infrastruktur

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Weighted Analysis of Microarray Experiments