Analyzing Multiple-Probe Microarray: Estimation and Application of Gene Expression Indexes
Article first published online: 26 JUL 2012
© 2012, The International Biometric Society
Volume 68, Issue 3, pages 784–792, September 2012
How to Cite
Maadooliat, M., Huang, J. Z. and Hu, J. (2012), Analyzing Multiple-Probe Microarray: Estimation and Application of Gene Expression Indexes. Biometrics, 68: 784–792. doi: 10.1111/j.1541-0420.2012.01727.x
- Issue published online: 26 SEP 2012
- Article first published online: 26 JUL 2012
- Received April 2010. Revised July 2011. Accepted November 2011.
- Differential expression detection;
- Fold change;
- Multivariate expression index;
- Profile likelihood;
- Transformation model
Summary 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).