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Received: 2006-04-04

Revision Accepted: 2006-05-31

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Journal of Zhejiang University SCIENCE B 2006 Vol.7 No.8 P.603-607


A robust statistical procedure to discover expression biomarkers using microarray genomic expression data

Author(s):  ZOU Yang-yun, YANG Jian, ZHU Jun

Affiliation(s):  Institute of Bioinformatics, Zhejiang University, Hangzhou 310029, China

Corresponding email(s):   jzhu@zju.edu.cn

Key Words:  Microarray, Biomarker, Henderson method III, Gene expression pattern, Mixed linear model

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ZOU Yang-yun, YANG Jian, ZHU Jun. A robust statistical procedure to discover expression biomarkers using microarray genomic expression data[J]. Journal of Zhejiang University Science B, 2006, 7(8): 603-607.

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author="ZOU Yang-yun, YANG Jian, ZHU Jun",
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%T A robust statistical procedure to discover expression biomarkers using microarray genomic expression data
%A ZOU Yang-yun
%A YANG Jian
%A ZHU Jun
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%P 603-607
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2006.B0603

T1 - A robust statistical procedure to discover expression biomarkers using microarray genomic expression data
A1 - ZOU Yang-yun
A1 - YANG Jian
A1 - ZHU Jun
J0 - Journal of Zhejiang University Science B
VL - 7
IS - 8
SP - 603
EP - 607
%@ 1673-1581
Y1 - 2006
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2006.B0603

microarray has become increasingly popular biotechnology in biological and medical researches, and has been widely applied in classification of treatment subtypes using expression patterns of biomarkers. We developed a statistical procedure to identify expression biomarkers for treatment subtype classification by constructing an F-statistic based on henderson method III. Monte Carlo simulations were conducted to examine the robustness and efficiency of the proposed method. Simulation results showed that our method could provide satisfying power of identifying differentially expressed genes (DEGs) with false discovery rate (FDR) lower than the given type I error rate. In addition, we analyzed a leukemia dataset collected from 38 leukemia patients with 27 samples diagnosed as acute lymphoblastic leukemia (ALL) and 11 samples as acute myeloid leukemia (AML). We compared our results with those from the methods of significance analysis of microarray (SAM) and microarray analysis of variance (MAANOVA). Among these three methods, only expression biomarkers identified by our method can precisely identify the three human acute leukemia subtypes.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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