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

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