Comparison of Li-Wong and loglinear mixed models for the statistical analysis of oligonucleotide arrays.
BACKGROUND
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.
Chu
Tzu-Ming
TM
Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA. tzu-ming.ch@sas.com
Weir
B S
BS
Wolfinger
Russell D
RD
D000465:Algorithms; D003198:Computer Simulation; D004247:DNA / Q000737:chemistry* / Q000235:genetics*; D020869:Gene Expression Profiling / Q000379:methods*; D016014:Linear Models; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
D004247:DNA