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.
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.
Authors: M Kenzelmann; S Maertens; M Hergenhahn; S Kueffer; A Hotz-Wagenblatt; L Li; S Wang; C Ittrich; T Lemberger; R Arribas; S Jonnakuty; M C Hollstein; W Schmid; N Gretz; H J Gröne; G Schütz Journal: Proc Natl Acad Sci U S A Date: 2007-04-03 Impact factor: 11.205
Authors: Sudarshan C Upadhya; Thuy K Smith; Peter A Brennan; Josyf C Mychaleckyj; Ashok N Hegde Journal: Neurochem Int Date: 2011-08-23 Impact factor: 3.921
Authors: Mitchell P Levesque; Teva Vernoux; Wolfgang Busch; Hongchang Cui; Jean Y Wang; Ikram Blilou; Hala Hassan; Keiji Nakajima; Noritaka Matsumoto; Jan U Lohmann; Ben Scheres; Philip N Benfey Journal: PLoS Biol Date: 2006-05-02 Impact factor: 8.029