Book chapter
A Two-Way Semilinear Model for Normalization and Analysis of Microarray Data
Springer Handbook of Engineering Statistics, pp.719-735
Springer London
2006
DOI: 10.1007/978-1-84628-288-1_40
Abstract
A proper normalization procedure ensures that the normalized intensity ratios provide meaningful measures of relative expression levels. We describe a two-way semilinear model (TW-SLM) two-way semilinear model (TW-SLM) for normalization and analysis of microarray data. This method does not make the usual assumptions underlying some of the existing methods. The TW-SLM also naturally incorporates uncertainty due to normalization into significance analysis of microarrays. We propose a semiparametric M-estimation method in the TW-SLM to estimate the normalization curves and the normalized expression values, and discuss several useful extensions of the TW-SLM. We describe a back-fitting algorithm for computation in the model. We illustrate the application of the TW-SLM by applying it to a microarray data set. We evaluate the performance of TW-SLM using simulation studies and consider theoretical results concerning the asymptotic distribution and rate of convergence of the least-squares estimators in the TW-SLM.
Details
- Title: Subtitle
- A Two-Way Semilinear Model for Normalization and Analysis of Microarray Data
- Creators
- Jian Huang - University of IowaCun-Hui Zhang - Rutgers University
- Contributors
- Hoang Pham (Editor)
- Resource Type
- Book chapter
- Publication Details
- Springer Handbook of Engineering Statistics, pp.719-735
- DOI
- 10.1007/978-1-84628-288-1_40
- Publisher
- Springer London; London
- Language
- English
- Date published
- 2006
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9983985921902771
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