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October 2010 Nonparametric estimation of genewise variance for microarray data
Jianqing Fan, Yang Feng, Yue S. Niu
Ann. Statist. 38(5): 2723-2750 (October 2010). DOI: 10.1214/10-AOS802

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 the number 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.

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Jianqing Fan. Yang Feng. Yue S. Niu. "Nonparametric estimation of genewise variance for microarray data." Ann. Statist. 38 (5) 2723 - 2750, October 2010. https://doi.org/10.1214/10-AOS802

Information

Published: October 2010
First available in Project Euclid: 11 July 2010

zbMATH: 1200.62133
MathSciNet: MR2722454
Digital Object Identifier: 10.1214/10-AOS802

Subjects:
Primary: 62G05
Secondary: 62P10

Keywords: correlation correction , gene selection , Genewise variance estimation , local linear regression , nonparametric model , validation test

Rights: Copyright © 2010 Institute of Mathematical Statistics

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Vol.38 • No. 5 • October 2010
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