Semilinear high-dimensional model for normalization of microarray data: A theoretical analysis and partial consistency

Jianqing Fan, Heng Peng, Tao Huang, Chiara Sabatti, Bruce A. Craig, Jian Huang, Cun Hui Zhang, Heping Zhang, Michael R. Kosorok, Ma Shuangge, Rober Tibshirani, Yi Ren

Research output: Journal article publicationReview articleAcademic researchpeer-review

Abstract

Normalization of microarray data is essential for removing experimental biases and revealing meaningful biological results. Motivated by a problem of normalizing microarray data, a semilinear in-slide model (SLIM) has been proposed. To aggregate information from other arrays, SLIM is generalized to account for across-array information, resulting in an even more dynamic semiparametric regression model. This model can be used to normalize microarray data even when there is no replication within an array. We demonstrate that this semiparametric model has a number of interesting features. The parametric component and the nonparametric component that are of primary interest can be consistently estimated, the former having a parametric rate and the latter having a nonparametric rate, whereas the nuisance parameters cannot be consistently estimated. This is an interesting extension of the partial consistent phenomena, which itself is of theoretical interest. The asymptotic normality for the parametric component and the rate of convergence for the nonparametric component are established. The results are augmented by simulation studies and illustrated by an application to the cDNA microarray analysis of neuroblastoma cells in response to the macrophage migration inhibitory factor.

Original languageEnglish
Pages (from-to)781-813
Number of pages33
JournalJournal of the American Statistical Association
Volume100
Issue number471
DOIs
Publication statusPublished - 1 Sep 2005
Externally publishedYes

Keywords

  • Aggregation
  • cDNA microarray
  • In-slide replications
  • Normalization
  • Partial consistency
  • Semiparametric models
  • SLIM

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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