title: A beta-mixture model for dimensionality reduction, sample classification and analysis.
abstract: BACKGROUND
  Patterns of genome-wide methylation vary between tissue types. For example, cancer tissue shows markedly different patterns from those of normal tissue. In this paper we propose a beta-mixture model to describe genome-wide methylation patterns based on probe data from methylation microarrays. The model takes dependencies between neighbour probe pairs into account and assumes three broad categories of methylation, low, medium and high. The model is described by 37 parameters, which reduces the dimensionality of a typical methylation microarray significantly. We used methylation microarray data from 42 colon cancer samples to assess the model.
  
  
  RESULTS
  Based on data from colon cancer samples we show that our model captures genome-wide characteristics of methylation patterns. We estimate the parameters of the model and show that they vary between different tissue types. Further, for each methylation probe the posterior probability of a methylation state (low, medium or high) is calculated and the probability that the state is correctly predicted is assessed. We demonstrate that the model can be applied to classify cancer tissue types accurately and that the model provides accessible and easily interpretable data summaries.
  
  
  CONCLUSIONS
  We have developed a beta-mixture model for methylation microarray data. The model substantially reduces the dimensionality of the data. It can be used for further analysis, such as sample classification or to detect changes in methylation status between different samples and tissues.
authors: Bioinformatics Research Centre, Aarhus University, C.F. Møllers Allé 8, DK-8000 Århus C, Denmark.
authors: Kirsti
authors: K
authors: Laurila
authors: Bodil
authors: B
authors: Oster
authors: Claus L
authors: CL
authors: Andersen
authors: Philippe
authors: P
authors: Lamy
authors: Torben
authors: T
authors: Orntoft
authors: Olli
authors: O
authors: Yli-Harja
authors: Carsten
authors: C
authors: Wiuf
references: Am J Pathol. 2001 Sep;159(3):1129-35
references: 11549606
references: PLoS One. 2010 Jan 13;5(1):e8564
references: 20084101
references: BMC Cancer. 2006 Aug 23;6:212
references: 16928264
references: Nat Genet. 2003 Jan;33(1):90-6
references: 12469123
references: Hum Mol Genet. 2007 Mar 1;16(5):547-54
references: 17339271
references: BMC Med Genomics. 2008 Sep 30;1:47
references: 18826610
references: J Gastroenterol Hepatol. 2005 Dec;20(12):1920-6
references: 16336454
references: Stat Appl Genet Mol Biol. 2009;8:Article 40
references: 19799559
references: BMC Bioinformatics. 2008 Oct 27;9:457
references: 18954440
references: Bioinformatics. 2004 Nov 1;20(16):2562-71
references: 15117756
references: Cancer Sci. 2010 Jan;101(1):36-45
references: 19891661
references: Neuropsychobiology. 2009;60(1):5-11
references: 19684418
references: Mol Cancer. 2009 Dec 16;8:124
references: 20015382
references: Nat Genet. 2009 Feb;41(2):178-186
references: 19151715
references: BMC Bioinformatics. 2009 Dec 09;10:404
references: 20003206
references: Cancer Res. 2004 Sep 15;64(18):6476-81
references: 15374957
references: Mol Carcinog. 2007 Oct;46(10):865-71
references: 17538946
references: PLoS One. 2009 Dec 18;4(12):e8274
references: 20019873
references: PLoS One. 2010 Feb 22;5(2):e9359
references: 20179752
references: BMC Bioinformatics. 2008 Sep 09;9:365
references: 18782434