A Two-Way Semilinear Model for Normalization and Analysis of Microarray Data

Research output: Chapter in Book/Report/Conference proceedingChapter

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.

Original languageAmerican English
Title of host publicationSpringer Handbooks
PublisherSpringer
Pages719-735
Number of pages17
DOIs
StatePublished - 2006

Publication series

NameSpringer Handbooks

ASJC Scopus subject areas

  • General

Keywords

  • Normalization Curve
  • Normalization Method
  • Polynomial Spline
  • Spiked Gene
  • Spline Method

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