Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/13799
Title: Fast Wavelet Based Functional Models for Transcriptome Analysis with Tiling Arrays
Authors: Clement, Lieven 
De Beuf, Kristof
THAS, Olivier 
Vuylsteke, Marnik
Irizarry, Rafael A.
Crainiceanu, Ciprian M.
Issue Date: 2012
Publisher: WALTER DE GRUYTER & CO
Source: 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.
Notes: [Clement, Lieven] Katholieke Univ Leuven, Louvain, Belgium. [Clement, Lieven] Univ Hasselt, Hasselt, Belgium. [De Beuf, Kristof; Thas, Olivier] London S Bank Univ, Dept Math Modelling Stat & Bioinformat, London, England. [Vuylsteke, Marnik] London S Bank Univ, VIB Dept Plant Syst Biol, London, England. [Irizarry, Rafael A.; Crainiceanu, Ciprian M.] Johns Hopkins Univ, Bloomberg Sch Publ Hlth, Dept Biostat, Baltimore, MD 21218 USA.
Keywords: Biochemistry & Molecular Biology; Statistics & Probability; tiling microarray; wavelets; adaptive regularization; transcript discovery; differential expression; genomics; Arabidopsis thaliana;tiling microarray; wavelets; adaptive regularization; transcript discovery; differential expression; genomics; Arabidopsis thaliana
Document URI: http://hdl.handle.net/1942/13799
ISSN: 2194-6302
e-ISSN: 1544-6115
DOI: 10.2202/1544-6115.1726
ISI #: 000305091700004
Category: A1
Type: Journal Contribution
Validations: ecoom 2013
Appears in Collections:Research publications

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