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Bioinformatics Advance Access published online on January 22, 2004
Bioinformatics, doi:10.1093/bioinformatics/btg435
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received April 22, 2003
Revised June 25, 2003
Accepted July 1, 2003

Article

Comparison of Li-Wong and loglinear mixed models for the statistical analysis of oligonucleotide arrays

Tzu-Ming Chu 1*, Bruce Weir 1, Russ Wolfinger 1

1 Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA; SAS Institute Inc., Cary, NC 27513, USA

* To whom correspondence should be addressed. E-mail: tzu-ming.chu{at}sas.com.


  Abstract

Motivation: Li and Wong have described some useful statistical models for probe-level, oligonucleotide array data based on a multiplicative parametrization. In earlier work, we proposed similar analysis-of-variance-style mixed models fit on a log scale. With only subtle differences in the specification of their mean and stochastic error components, a question arises as to whether these models could lead to varying conclusions in practical application.

Results: In this paper, we provide an empirical comparison of the two models using a real data set, and find the models perform quite similarly across most genes, but with some interesting and important distinctions. We also present results from a simulation study designed to assess inferential properties of the models, and propose a modified test statistic for the Li-Wong model that provides an improvement in Type 1 error control. Advantages of both methods include the ability to directly assess and account for key sources of variability in the chip data and a means to automate statistical quality control.

Availability: The Li-Wong models are available in dChip: http://www.biostat.harvard.edu/complab/dchip/, and both methods will be commercially available in the forthcoming SAS Microarray Solution.

Supplementary information: Supplementary material is available at http://statgen.ncsu.edu/ggibson/Pubs.htm


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