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Analysis of tiling array expression studies with flexible designs in Bioconductor (waveTiling)

Kristof De Beuf1*, Peter Pipelers1, Megan Andriankaja23, Olivier Thas1, Dirk Inzé23, Ciprian Crainiceanu4 and Lieven Clement15*

Author Affiliations

1 Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, B9000 Ghent, Belgium

2 Department of Plant Systems Biology, Flanders Institute for Biotechnology, Ghent, Belgium

3 Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium

4 Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St., Baltimore, MD, USA

5 Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Katholieke Universiteit Leuven and Universiteit Hasselt, Kapucijnenvoer 35, Blok D, bus 7001B3000 Leuven, Belgium

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BMC Bioinformatics 2012, 13:234  doi:10.1186/1471-2105-13-234

Published: 14 September 2012



Existing statistical methods for tiling array transcriptome data either focus on transcript discovery in one biological or experimental condition or on the detection of differential expression between two conditions. Increasingly often, however, biologists are interested in time-course studies, studies with more than two conditions or even multiple-factor studies. As these studies are currently analyzed with the traditional microarray analysis techniques, they do not exploit the genome-wide nature of tiling array data to its full potential.


We present an R Bioconductor package, waveTiling, which implements a wavelet-based model for analyzing transcriptome data and extends it towards more complex experimental designs. With waveTiling the user is able to discover (1) group-wise expressed regions, (2) differentially expressed regions between any two groups in single-factor studies and in (3) multifactorial designs. Moreover, for time-course experiments it is also possible to detect (4) linear time effects and (5) a circadian rhythm of transcripts. By considering the expression values of the individual tiling probes as a function of genomic position, effect regions can be detected regardless of existing annotation. Three case studies with different experimental set-ups illustrate the use and the flexibility of the model-based transcriptome analysis.


The waveTiling package provides the user with a convenient tool for the analysis of tiling array trancriptome data for a multitude of experimental set-ups. Regardless of the study design, the probe-wise analysis allows for the detection of transcriptional effects in both exonic, intronic and intergenic regions, without prior consultation of existing annotation.