Multiple Testing for Pattern Identification, With Applications to Microarray Time-Course Experiments
In time-course experiments, it is often desirable to identify genes that exhibit a specific pattern of differential expression over time and thus gain insights into the mechanisms of the underlying biological processes. Two challenging issues in the pattern identification problem are: (i) how to com...
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| Vydáno v: | Journal of the American Statistical Association Ročník 106; číslo 493; s. 73 - 88 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Alexandria, VA
American Statistical Association
01.03.2011
Taylor & Francis Ltd |
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| ISSN: | 0162-1459, 1537-274X |
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| Abstract | In time-course experiments, it is often desirable to identify genes that exhibit a specific pattern of differential expression over time and thus gain insights into the mechanisms of the underlying biological processes. Two challenging issues in the pattern identification problem are: (i) how to combine the simultaneous inferences across multiple time points and (ii) how to control the multiplicity while accounting for the strong dependence. We formulate a compound decision-theoretic framework for set-wise multiple testing and propose a data-driven procedure that aims to minimize the missed set rate subject to a constraint on the false set rate. The hidden Markov model proposed in Yuan and Kendziorski (2006) is generalized to capture the temporal correlation in the gene expression data. Both theoretical and numerical results are presented to show that our data-driven procedure controls the multiplicity, provides an optimal way of combining simultaneous inferences across multiple time points, and greatly improves the conventional combined p-value methods. In particular, we demonstrate our method in an application to a study of systemic inflammation in humans for detecting early and late response genes. |
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| AbstractList | In time-course experiments, it is often desirable to identify genes that exhibit a specific pattern of differential expression over time and thus gain insights into the mechanisms of the underlying biological processes. Two challenging issues in the pattern identification problem are: (i) how to combine the simultaneous inferences across multiple time points and (ii) how to control the multiplicity while accounting for the strong dependence. We formulate a compound decision-theoretic framework for set-wise multiple testing and propose a data-driven procedure that aims to minimize the missed set rate subject to a constraint on the false set rate. The hidden Markov model proposed in Yuan and Kendziorski (2006) is generalized to capture the temporal correlation in the gene expression data. Both theoretical and numerical results are presented to show that our data-driven procedure controls the multiplicity, provides an optimal way of combining simultaneous inferences across multiple time points, and greatly improves the conventional combined p-value methods. In particular, we demonstrate our method in an application to a study of systemic inflammation in humans for detecting early and late response genes. [PUBLICATION ABSTRACT] In time-course experiments, it is often desirable to identify genes that exhibit a specific pattern of differential expression over time and thus gain insights into the mechanisms of the underlying biological processes. Two challenging issues in the pattern identification problem are: (i) how to combine the simultaneous inferences across multiple time points and (ii) how to control the multiplicity while accounting for the strong dependence. We formulate a compound decision-theoretic framework for set-wise multiple testing and propose a data-driven procedure that aims to minimize the missed set rate subject to a constraint on the false set rate. The hidden Markov model proposed in Yuan and Kendziorski (2006) is generalized to capture the temporal correlation in the gene expression data. Both theoretical and numerical results are presented to show that our data-driven procedure controls the multiplicity, provides an optimal way of combining simultaneous inferences across multiple time points, and greatly improves the conventional combined p-value methods. In particular, we demonstrate our method in an application to a study of systemic inflammation in humans for detecting early and late response genes. |
| Author | Wei, Zhi Sun, Wenguang |
| Author_xml | – sequence: 1 givenname: Wenguang surname: Sun fullname: Sun, Wenguang – sequence: 2 givenname: Zhi surname: Wei fullname: Wei, Zhi |
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| Keywords | Human Correlation Microarray time-course data Statistical association Statistical estimation Statistical decision Multivariate analysis Microarray Conjunction and partial conjunction tests Multiple decision Statistical method Statistical test Compound decision problem Correlation analysis Decision theory Hidden Markov models Simultaneous set-wise testing Hidden Markov model False discovery rate P value |
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| Title | Multiple Testing for Pattern Identification, With Applications to Microarray Time-Course Experiments |
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