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Abstract Details
Activity Number:
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404
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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Abstract - #302965 |
Title:
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A Generalized Linear Model Framework for Underdispersed Count Data with an Application to MiRNA Data
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Author(s):
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Lieven Clement*+ and Peter Pipelers and Olivier Thas and Jean-Pierre Ottoy
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Companies:
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Katholieke Universiteit Leuven and Ghent University and Ghent University and Ghent University
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Address:
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Kapucijnenvoer 35, Leuven, B-3000, Belgium
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Keywords:
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Poisson regression ;
underdispersion ;
qPCR ;
GAM ;
miRNA ;
Newton-Raphson
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Abstract:
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miRNA's play an important role in gene regulation and they are often measured by quantitative PCR (qPCR). The output of qPCR is the number of cycles, Cq-value, that is needed for a certain target to exceed a threshold. The Cq-values can be considered as counts. But, they seem to be underdispersed with respect to the Poisson distribution, i.e. their variance is less than the mean. There exist a rich variety of regression frameworks for overdispersed count data. The choice of alternative distributions for underdispersed count data, however, is rather limited and regression frameworks are lacking. We introduce a tilted Poisson distribution for underdispersed counts. It has two parameters ? and ? for location and scale, respectively. We embed the tilted Poisson distribution in a generalized linear model (GLM) framework. Both parameters ? and ? can be modeled in function of covariates by using linear and/or smooth components. We propose a Newton-Raphson algorithm for parameter estimation and implement it as a new distributional family for the R-package VGAM. We illustrate our approach in a case study for assessing differential expression of miRNA's in the presence of confounders.
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