Gene expression index estimation is an essential step in analyzing multiple probe microarray data. Various modeling methods have been proposed in this area. Amidst all, a popular method proposed in Li and Wong (2001) is based on a multiplicative model, which is similar to the additive model discussed in Irizarry et al. (2003a) at the logarithm scale. Along this line, Hu et al. (2006) proposed data transformation to improve expression index estimation based on an ad hoc entropy criteria and naive grid search approach. In this work, we re-examined this problem using a new profile likelihood-based transformation estimation approach that is more statistically elegant and computationally efficient. We demonstrate the applicability of the proposed method using a benchmark Affymetrix U95A spiked-in experiment. Moreover, We introduced a new multivariate expression index and used the empirical study to shows its promise in terms of improving model fitting and power of detecting differential expression over the commonly used univariate expression index. As the other important content of the work, we discussed two generally encountered practical issues in application of gene expression index: normalization and summary statistic used for detecting differential expression. Our empirical study shows somewhat different findings from the MAQC project (MAQC, 2006).
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-CI-016-04
Acknowledgements: We would like to thank an AE and a referee for their constructive comments. Mehdi Maadooliat and Jianhua Hu were supported by the National Science Foundation Grant DMS-0706818, National Institutes of Health Grants R01GM080503-01A1, R21CA129671, Cancer Center Support Grant P30 CA016672, and National Cancer Institute CA97007. Jianhua Z. Huang was supported by NSF (DMS-0606580, DMS-0907170), NCI (CA57030), and King Abdullah University of Science and Technology (KUS-CI-016-04). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.