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A semiparametric unified approach for the detection of differential gene expression in microarrays

Jan De Neve (UGent) , Olivier Thas (UGent) , Lieven Clement (UGent) and Jean-Pierre Ottoy (UGent)
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Abstract
A general method is proposed for detecting differential genes in high density oligonucleotide microarrays. It is a unified approach in the sense that it integrates the three preprocessing steps and the statistical testing methods into one semiparametric model. An important characteristic is that no stringent assumptions are imposed on the background correction and normalization steps. Instead of focusing on mean differences in gene expression, we formulate the model in terms of stochastic ordering. In particular, probabilities $P(Y_1 < Y_2 )$, with $Y_i$ the intensity of a gene in group $i$ ($i = 1, 2$), are modeled in terms of predictor variables. We present some theoretical results and spike-in studies are considered for comparing the performance of this new method with existing methods. Finally we apply the new method to a publicly available data set.
Keywords
microarrays, stochastic ordering, differential gene expression, semi-parametric inference

Citation

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Chicago
De Neve, Jan, Olivier Thas, Lieven Clement, and Jean-Pierre Ottoy. 2009. “A Semiparametric Unified Approach for the Detection of Differential Gene Expression in Microarrays.” In Joint Statistical Meetings, Abstracts.
APA
De Neve, Jan, Thas, O., Clement, L., & Ottoy, J.-P. (2009). A semiparametric unified approach for the detection of differential gene expression in microarrays. Joint Statistical Meetings, Abstracts. Presented at the 2009 Joint Statistical Meetings (JSM 2009).
Vancouver
1.
De Neve J, Thas O, Clement L, Ottoy J-P. A semiparametric unified approach for the detection of differential gene expression in microarrays. Joint Statistical Meetings, Abstracts. 2009.
MLA
De Neve, Jan, Olivier Thas, Lieven Clement, et al. “A Semiparametric Unified Approach for the Detection of Differential Gene Expression in Microarrays.” Joint Statistical Meetings, Abstracts. 2009. Print.
@inproceedings{2129376,
  abstract     = {A general method is proposed for detecting differential genes in high density oligonucleotide microarrays. It is a unified approach in the sense that it integrates the three preprocessing steps and the statistical testing methods into one semiparametric model. An important characteristic is that no stringent assumptions are imposed on the background correction and normalization steps. Instead of focusing on mean differences in gene expression, we formulate the model in terms of stochastic ordering. In particular, probabilities \$P(Y\_1 {\textlangle} Y\_2 )\$, with \$Y\_i\$ the intensity of a gene in group \$i\$ (\$i = 1, 2\$), are modeled in terms of predictor variables. We present some theoretical results and spike-in studies are considered for comparing the performance of this new method with existing methods. Finally we apply the new method to a publicly available data set.},
  author       = {De Neve, Jan and Thas, Olivier and Clement, Lieven and Ottoy, Jean-Pierre},
  booktitle    = {Joint Statistical Meetings, Abstracts},
  keyword      = {microarrays,stochastic ordering,differential gene expression,semi-parametric inference},
  language     = {eng},
  location     = {Washington, DC, USA},
  title        = {A semiparametric unified approach for the detection of differential gene expression in microarrays},
  year         = {2009},
}