Semilinear High-Dimensional Model for Normalization of Microarray Data: A Theoretical Analysis and Partial Consistency

Jianqing Fan*, Heng Peng, Tao Huang

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

62 Citations (Scopus)


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)781-796
Number of pages16
JournalJournal of the American Statistical Association
Issue number471
Publication statusPublished - Sept 2005

Scopus Subject Areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

User-Defined Keywords

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


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