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Empirical Bayes analysis of unreplicated microarray data

Authors
Cho, HyungJunKang, JaewooLee, Jae K.
Issue Date
8월-2009
Publisher
SPRINGER HEIDELBERG
Keywords
Microarray data; Empirical Bayes; Markov chain Monte Carlo; No replication
Citation
COMPUTATIONAL STATISTICS, v.24, no.3, pp.393 - 408
Indexed
SCIE
SCOPUS
Journal Title
COMPUTATIONAL STATISTICS
Volume
24
Number
3
Start Page
393
End Page
408
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/119608
DOI
10.1007/s00180-008-0133-9
ISSN
0943-4062
Abstract
Because of the high costs of microarray experiments and the availability of only limited biological materials, microarray experiments are often performed with a small number of replicates. Investigators, therefore, often have to perform their experiments with low replication or without replication. However, the heterogeneous error variability observed in microarray experiments increases the difficulty in analyzing microarray data without replication. No current analysis techniques are practically applicable to such microarray data analysis. We here introduce a statistical method, the so-called unreplicated heterogeneous error model (UHEM) for the microarray data analysis without replication. This method is possible by utilizing many adjacent-intensity genes for estimating local error variance after nonparametric elimination of differentially expressed genes between different biological conditions. We compared the performance of UHEM with three empirical Bayes prior specification methods: between-condition local pooled error, pseudo standard error, or adaptive standard error-based HEM. We found that our unreplicated HEM method is effective for the microarray data analysis when replication of an array experiment is impractical or prohibited.
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College of Political Science & Economics > Department of Statistics > 1. Journal Articles
Graduate School > Department of Computer Science and Engineering > 1. Journal Articles

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