title: Analysis of tiling array expression studies with flexible designs in Bioconductor (waveTiling).
abstract: BACKGROUND
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, B9000 Ghent, Belgium. kristof.debeuf@ugent.be
authors.forename: Kristof
authors.initials: K
authors.lastname: De Beuf
authors.forename: Peter
authors.initials: P
authors.lastname: Pipelers
authors.forename: Megan
authors.initials: M
authors.lastname: Andriankaja
authors.forename: Olivier
authors.initials: O
authors.lastname: Thas
authors.forename: Dirk
authors.initials: D
authors.lastname: Inzé
authors.forename: Ciprian
authors.initials: C
authors.lastname: Crainiceanu
authors.forename: Lieven
authors.initials: L
authors.lastname: Clement
mesh_terms: D003627:Data Interpretation, Statistical; D005091:Exons; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D016678:Genome; D023281:Genomics / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D012984:Software; D059467:Transcriptome