Improving the power to detect differentially expressed genes in comparative microarray experiments by including information from self-self hybridizations

Arief Gusnanto; Brian Tom; Philippa Burns; Iain Macaulay; Daphne C. Thijssen-Timmer; Marloes R. Tijssen; Cordelia Langford; Nicholas Watkins; Willem Ouwehand; Carlo Berzuini; Frank Dudbridge

doi:10.1016/j.compbiolchem.2007.03.005

Improving the power to detect differentially expressed genes in comparative microarray experiments by including information from self-self hybridizations

Arief Gusnanto, Brian Tom, Philippa Burns, Iain Macaulay, Daphne C. Thijssen-Timmer, Marloes R. Tijssen, Cordelia Langford, Nicholas Watkins, Willem Ouwehand, Carlo Berzuini, Frank Dudbridge

Research output: Contribution to journal › Article › peer-review

Abstract

Our ability to detect differentially expressed genes in a microarray experiment can be hampered when the number of biological samples of interest is limited. In this situation, we propose the use of information from self-self hybridizations to acuminate our inference of differential expression. A unified modelling strategy is developed to allow better estimation of the error variance. This principle is similar to the use of a pooled variance estimate in the two-sample t-test. The results from real dataset examples suggest that we can detect more genes that are differentially expressed in the combined models. Our simulation study provides evidence that this method increases sensitivity compared to using the information from comparative hybridizations alone, given the same control for false discovery rate. The largest increase in sensitivity occurs when the amount of information in the comparative hybridization is limited. © 2007 Elsevier Ltd. All rights reserved.

Original language	English
Pages (from-to)	178-185
Number of pages	7
Journal	Computational Biology and Chemistry
Volume	31
Issue number	3
DOIs	https://doi.org/10.1016/j.compbiolchem.2007.03.005
Publication status	Published - Jun 2007

Keywords

Differentially expressed
Gene expressions
Linear models
Microarray
Self-self hybridizations

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10.1016/j.compbiolchem.2007.03.005

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Gusnanto, A., Tom, B., Burns, P., Macaulay, I., Thijssen-Timmer, D. C., Tijssen, M. R., Langford, C., Watkins, N., Ouwehand, W., Berzuini, C., & Dudbridge, F. (2007). Improving the power to detect differentially expressed genes in comparative microarray experiments by including information from self-self hybridizations. Computational Biology and Chemistry, 31(3), 178-185. https://doi.org/10.1016/j.compbiolchem.2007.03.005

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title = "Improving the power to detect differentially expressed genes in comparative microarray experiments by including information from self-self hybridizations",

abstract = "Our ability to detect differentially expressed genes in a microarray experiment can be hampered when the number of biological samples of interest is limited. In this situation, we propose the use of information from self-self hybridizations to acuminate our inference of differential expression. A unified modelling strategy is developed to allow better estimation of the error variance. This principle is similar to the use of a pooled variance estimate in the two-sample t-test. The results from real dataset examples suggest that we can detect more genes that are differentially expressed in the combined models. Our simulation study provides evidence that this method increases sensitivity compared to using the information from comparative hybridizations alone, given the same control for false discovery rate. The largest increase in sensitivity occurs when the amount of information in the comparative hybridization is limited. {\textcopyright} 2007 Elsevier Ltd. All rights reserved.",

keywords = "Differentially expressed, Gene expressions, Linear models, Microarray, Self-self hybridizations",

author = "Arief Gusnanto and Brian Tom and Philippa Burns and Iain Macaulay and Thijssen-Timmer, {Daphne C.} and Tijssen, {Marloes R.} and Cordelia Langford and Nicholas Watkins and Willem Ouwehand and Carlo Berzuini and Frank Dudbridge",

year = "2007",

month = jun,

doi = "10.1016/j.compbiolchem.2007.03.005",

language = "English",

volume = "31",

pages = "178--185",

journal = "Computational Biology and Chemistry",

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Gusnanto, A, Tom, B, Burns, P, Macaulay, I, Thijssen-Timmer, DC, Tijssen, MR, Langford, C, Watkins, N, Ouwehand, W, Berzuini, C & Dudbridge, F 2007, 'Improving the power to detect differentially expressed genes in comparative microarray experiments by including information from self-self hybridizations', Computational Biology and Chemistry, vol. 31, no. 3, pp. 178-185. https://doi.org/10.1016/j.compbiolchem.2007.03.005

TY - JOUR

T1 - Improving the power to detect differentially expressed genes in comparative microarray experiments by including information from self-self hybridizations

AU - Gusnanto, Arief

AU - Tom, Brian

AU - Burns, Philippa

AU - Macaulay, Iain

AU - Thijssen-Timmer, Daphne C.

AU - Tijssen, Marloes R.

AU - Langford, Cordelia

AU - Watkins, Nicholas

AU - Ouwehand, Willem

AU - Berzuini, Carlo

AU - Dudbridge, Frank

PY - 2007/6

Y1 - 2007/6

N2 - Our ability to detect differentially expressed genes in a microarray experiment can be hampered when the number of biological samples of interest is limited. In this situation, we propose the use of information from self-self hybridizations to acuminate our inference of differential expression. A unified modelling strategy is developed to allow better estimation of the error variance. This principle is similar to the use of a pooled variance estimate in the two-sample t-test. The results from real dataset examples suggest that we can detect more genes that are differentially expressed in the combined models. Our simulation study provides evidence that this method increases sensitivity compared to using the information from comparative hybridizations alone, given the same control for false discovery rate. The largest increase in sensitivity occurs when the amount of information in the comparative hybridization is limited. © 2007 Elsevier Ltd. All rights reserved.

AB - Our ability to detect differentially expressed genes in a microarray experiment can be hampered when the number of biological samples of interest is limited. In this situation, we propose the use of information from self-self hybridizations to acuminate our inference of differential expression. A unified modelling strategy is developed to allow better estimation of the error variance. This principle is similar to the use of a pooled variance estimate in the two-sample t-test. The results from real dataset examples suggest that we can detect more genes that are differentially expressed in the combined models. Our simulation study provides evidence that this method increases sensitivity compared to using the information from comparative hybridizations alone, given the same control for false discovery rate. The largest increase in sensitivity occurs when the amount of information in the comparative hybridization is limited. © 2007 Elsevier Ltd. All rights reserved.

KW - Differentially expressed

KW - Gene expressions

KW - Linear models

KW - Microarray

KW - Self-self hybridizations

U2 - 10.1016/j.compbiolchem.2007.03.005

DO - 10.1016/j.compbiolchem.2007.03.005

M3 - Article

SN - 1476-9271

VL - 31

SP - 178

EP - 185

JO - Computational Biology and Chemistry

JF - Computational Biology and Chemistry

IS - 3

ER -

Improving the power to detect differentially expressed genes in comparative microarray experiments by including information from self-self hybridizations

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Keywords

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