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Fast wavelet based functional models for transcriptome analysis with tiling arrays

Lieven Clement UGent, Kristof De Beuf UGent, Olivier Thas UGent, Marnik Vuylsteke UGent, Rafael Irizarry and Ciprian M Crainiceanu (2012) STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY. 11(1).
abstract
For a better understanding of the biology of an organism, a complete description is needed of all regions of the genome that are actively transcribed. Tiling arrays are used for this purpose. They allow for the discovery of novel transcripts and the assessment of differential expression between two or more experimental conditions such as genotype, treatment, tissue, etc. In tiling array literature, many efforts are devoted to transcript discovery, whereas more recent developments also focus on differential expression. To our knowledge, however, no methods for tiling arrays have been described that can simultaneously assess transcript discovery and identify differentially expressed transcripts. In this paper, we adopt wavelet based functional models to the context of tiling arrays. The high dimensionality of the data triggered us to avoid inference based on Bayesian MCMC methods. Instead, we introduce a fast empirical Bayes method that provides adaptive regularization of the functional effects. A simulation study and a case study illustrate that our approach is well suited for the simultaneous assessment of transcript discovery and differential expression in tiling array studies, and that it outperforms methods that accomplish only one of these tasks.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
transcript discovery, Arabidopsis thaliana, differential expression, genomics, tiling microarray, wavelets, adaptive regularization
journal title
STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY
Stat. Appl. Genet. Mol. Biol.
volume
11
issue
1
article_number
4
pages
36 pages
Web of Science type
Article
Web of Science id
000305091700004
ISSN
1544-6115
DOI
10.2202/1544-6115.1726
project
Bioinformatics: from nucleotids to networks (N2N)
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
2039860
handle
http://hdl.handle.net/1854/LU-2039860
date created
2012-02-22 10:12:10
date last changed
2013-04-16 12:51:54
@article{2039860,
  abstract     = {For a better understanding of the biology of an organism, a complete description is needed of all regions of the genome that are actively transcribed. Tiling arrays are used for this purpose. They allow for the discovery of novel transcripts and the assessment of differential expression between two or more experimental conditions such as genotype, treatment, tissue, etc. In tiling array literature, many efforts are devoted to transcript discovery, whereas more recent developments also focus on differential expression. To our knowledge, however, no methods for tiling arrays have been described that can simultaneously assess transcript discovery and identify differentially expressed transcripts. In this paper, we adopt wavelet based functional models to the context of tiling arrays. The high dimensionality of the data triggered us to avoid inference based on Bayesian MCMC methods. Instead, we introduce a fast empirical Bayes method that provides adaptive regularization of the functional effects. A simulation study and a case study illustrate that our approach is well suited for the simultaneous assessment of transcript discovery and differential expression in tiling array studies, and that it outperforms methods that accomplish only one of these tasks.},
  articleno    = {4},
  author       = {Clement, Lieven and De Beuf, Kristof and Thas, Olivier and Vuylsteke, Marnik and Irizarry, Rafael and Crainiceanu, Ciprian M},
  issn         = {1544-6115},
  journal      = {STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY},
  keyword      = {transcript discovery,Arabidopsis thaliana,differential expression,genomics,tiling microarray,wavelets,adaptive regularization},
  language     = {eng},
  number       = {1},
  pages        = {36},
  title        = {Fast wavelet based functional models for transcriptome analysis with tiling arrays},
  url          = {http://dx.doi.org/10.2202/1544-6115.1726},
  volume       = {11},
  year         = {2012},
}

Chicago
Clement, Lieven, Kristof De Beuf, Olivier Thas, Marnik Vuylsteke, Rafael Irizarry, and Ciprian M Crainiceanu. 2012. “Fast Wavelet Based Functional Models for Transcriptome Analysis with Tiling Arrays.” Statistical Applications in Genetics and Molecular Biology 11 (1).
APA
Clement, L., De Beuf, K., Thas, O., Vuylsteke, M., Irizarry, R., & Crainiceanu, C. M. (2012). Fast wavelet based functional models for transcriptome analysis with tiling arrays. STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY, 11(1).
Vancouver
1.
Clement L, De Beuf K, Thas O, Vuylsteke M, Irizarry R, Crainiceanu CM. Fast wavelet based functional models for transcriptome analysis with tiling arrays. STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY. 2012;11(1).
MLA
Clement, Lieven, Kristof De Beuf, Olivier Thas, et al. “Fast Wavelet Based Functional Models for Transcriptome Analysis with Tiling Arrays.” STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY 11.1 (2012): n. pag. Print.