Nonparametric estimation of genewise variance for microarray data

Jianqing Fan, Yang Feng, Yue S. Niu

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

Estimation of genewise variance arises from two important applications in microarray data analysis: selecting significantly differentially expressed genes and validation tests for normalization of microarray data. We approach the problem by introducing a two-way nonparametric model, which is an extension of the famous Neyman-Scott model and is applicable beyond microarray data. The problem itself poses interesting challenges because thenumber of nuisance parameters is proportional to the sample size and it is not obvious how the variance function can be estimated when measurements are correlated. In such a high-dimensional nonparametric problem, we proposed two novel nonparametric estimators for genewise variance function and semiparametric estimators for measurement correlation, via solving a system of nonlinear equations. Their asymptotic normality is established. The finite sample property is demonstrated by simulation studies. The estimators also improve the power of the tests for detecting statistically differentially expressed genes. The methodology is illustrated by the data from microarray quality control (MAQC) project.

Original languageEnglish (US)
Pages (from-to)2723-2750
Number of pages28
JournalAnnals of Statistics
Volume38
Issue number5
DOIs
StatePublished - Oct 1 2010

Keywords

  • Correlation correction
  • Gene selection
  • Genewise variance estimation
  • Local linear regression
  • Nonparametric model
  • Validation test

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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