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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 (2009) Joint Statistical Meetings, Abstracts.
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.
Please use this url to cite or link to this publication:
author
organization
year
type
conference
publication status
published
subject
keyword
microarrays, stochastic ordering, differential gene expression, semi-parametric inference
in
Joint Statistical Meetings, Abstracts
conference name
2009 Joint Statistical Meetings (JSM 2009)
conference location
Washington, DC, USA
conference start
2009-08-01
conference end
2009-08-06
language
English
UGent publication?
yes
classification
C3
id
2129376
handle
http://hdl.handle.net/1854/LU-2129376
date created
2012-06-01 15:40:06
date last changed
2012-06-14 13:44:30
@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},
}

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.