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タイトル: | Bayesian ranking and selection methods using hierarchical mixture models in microarray studies. |
著者: | Noma, Hisashi Matsui, Shigeyuki Omori, Takashi Sato, Tosiya https://orcid.org/0000-0001-9830-013X (unconfirmed) |
著者名の別形: | 野間, 久史 |
キーワード: | Empirical Bayes Gene expression Hierarchical mixture models Microarrays Ranking and selection |
発行日: | Apr-2010 |
出版者: | Oxford University Press |
誌名: | Biostatistics |
巻: | 11 |
号: | 2 |
開始ページ: | 281 |
終了ページ: | 289 |
抄録: | The main purpose of microarray studies is screening to identify differentially expressed genes as candidates for further investigation. Because of limited resources in this stage, prioritizing or ranking genes is a relevant statistical task in microarray studies. In this article, we develop 3 empirical Bayes methods for gene ranking on the basis of differential expression, using hierarchical mixture models. These methods are based on (i) minimizing mean squared errors of estimation for parameters, (ii) minimizing mean squared errors of estimation for ranks of parameters, and (iii) maximizing sensitivity in selecting prespecified numbers of differential genes, with the largest effect. Our methods incorporate the mixture structures of differential and nondifferential components in empirical Bayes models to allow information borrowing across differential genes, with separation from nuisance, nondifferential genes. The accuracy of our ranking methods is compared with that of conventional methods through simulation studies. An application to a clinical study for breast cancer is provided. |
著作権等: | © The Author 2009. Published by Oxford University Press. 許諾条件により本文は2011-05-01に公開 この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。 This is not the published version. Please cite only the published version. |
URI: | http://hdl.handle.net/2433/139478 |
DOI(出版社版): | 10.1093/biostatistics/kxp047 |
PubMed ID: | 19946026 |
出現コレクション: | 学術雑誌掲載論文等 |
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